---
_id: '6637'
abstract:
- lang: eng
text: The environment changes constantly at various time scales and, in order to
survive, species need to keep adapting. Whether these species succeed in avoiding
extinction is a major evolutionary question. Using a multilocus evolutionary model
of a mutation‐limited population adapting under strong selection, we investigate
the effects of the frequency of environmental fluctuations on adaptation. Our
results rely on an “adaptive‐walk” approximation and use mathematical methods
from evolutionary computation theory to investigate the interplay between fluctuation
frequency, the similarity of environments, and the number of loci contributing
to adaptation. First, we assume a linear additive fitness function, but later
generalize our results to include several types of epistasis. We show that frequent
environmental changes prevent populations from reaching a fitness peak, but they
may also prevent the large fitness loss that occurs after a single environmental
change. Thus, the population can survive, although not thrive, in a wide range
of conditions. Furthermore, we show that in a frequently changing environment,
the similarity of threats that a population faces affects the level of adaptation
that it is able to achieve. We check and supplement our analytical results with
simulations.
acknowledgement: The authors would like to thank to Tiago Paixao and Nick Barton for
useful comments and advice.
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Barbora
full_name: Trubenova, Barbora
id: 42302D54-F248-11E8-B48F-1D18A9856A87
last_name: Trubenova
orcid: 0000-0002-6873-2967
- first_name: 'Martin '
full_name: 'Krejca, Martin '
last_name: Krejca
- first_name: Per Kristian
full_name: Lehre, Per Kristian
last_name: Lehre
- first_name: Timo
full_name: Kötzing, Timo
last_name: Kötzing
citation:
ama: 'Trubenova B, Krejca M, Lehre PK, Kötzing T. Surfing on the seascape: Adaptation
in a changing environment. Evolution. 2019;73(7):1356-1374. doi:10.1111/evo.13784'
apa: 'Trubenova, B., Krejca, M., Lehre, P. K., & Kötzing, T. (2019). Surfing
on the seascape: Adaptation in a changing environment. Evolution. Wiley.
https://doi.org/10.1111/evo.13784'
chicago: 'Trubenova, Barbora, Martin Krejca, Per Kristian Lehre, and Timo Kötzing.
“Surfing on the Seascape: Adaptation in a Changing Environment.” Evolution.
Wiley, 2019. https://doi.org/10.1111/evo.13784.'
ieee: 'B. Trubenova, M. Krejca, P. K. Lehre, and T. Kötzing, “Surfing on the seascape:
Adaptation in a changing environment,” Evolution, vol. 73, no. 7. Wiley,
pp. 1356–1374, 2019.'
ista: 'Trubenova B, Krejca M, Lehre PK, Kötzing T. 2019. Surfing on the seascape:
Adaptation in a changing environment. Evolution. 73(7), 1356–1374.'
mla: 'Trubenova, Barbora, et al. “Surfing on the Seascape: Adaptation in a Changing
Environment.” Evolution, vol. 73, no. 7, Wiley, 2019, pp. 1356–74, doi:10.1111/evo.13784.'
short: B. Trubenova, M. Krejca, P.K. Lehre, T. Kötzing, Evolution 73 (2019) 1356–1374.
date_created: 2019-07-14T21:59:20Z
date_published: 2019-07-01T00:00:00Z
date_updated: 2023-08-29T06:31:14Z
day: '01'
ddc:
- '576'
department:
- _id: NiBa
doi: 10.1111/evo.13784
ec_funded: 1
external_id:
isi:
- '000474031600001'
file:
- access_level: open_access
checksum: 9831ca65def2d62498c7b08338b6d237
content_type: application/pdf
creator: apreinsp
date_created: 2019-07-16T06:08:31Z
date_updated: 2020-07-14T12:47:34Z
file_id: '6643'
file_name: 2019_Evolution_TrubenovaBarbora.pdf
file_size: 815416
relation: main_file
file_date_updated: 2020-07-14T12:47:34Z
has_accepted_license: '1'
intvolume: ' 73'
isi: 1
issue: '7'
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
month: '07'
oa: 1
oa_version: Published Version
page: 1356-1374
project:
- _id: 25AEDD42-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '704172'
name: Rate of Adaptation in Changing Environment
- _id: 25B1EC9E-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '618091'
name: Speed of Adaptation in Population Genetics and Evolutionary Computation
publication: Evolution
publication_status: published
publisher: Wiley
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Surfing on the seascape: Adaptation in a changing environment'
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 73
year: '2019'
...
---
_id: '723'
abstract:
- lang: eng
text: Escaping local optima is one of the major obstacles to function optimisation.
Using the metaphor of a fitness landscape, local optima correspond to hills separated
by fitness valleys that have to be overcome. We define a class of fitness valleys
of tunable difficulty by considering their length, representing the Hamming path
between the two optima and their depth, the drop in fitness. For this function
class we present a runtime comparison between stochastic search algorithms using
different search strategies. The (1+1) EA is a simple and well-studied evolutionary
algorithm that has to jump across the valley to a point of higher fitness because
it does not accept worsening moves (elitism). In contrast, the Metropolis algorithm
and the Strong Selection Weak Mutation (SSWM) algorithm, a famous process in population
genetics, are both able to cross the fitness valley by accepting worsening moves.
We show that the runtime of the (1+1) EA depends critically on the length of the
valley while the runtimes of the non-elitist algorithms depend crucially on the
depth of the valley. Moreover, we show that both SSWM and Metropolis can also
efficiently optimise a rugged function consisting of consecutive valleys.
article_processing_charge: No
author:
- first_name: Pietro
full_name: Oliveto, Pietro
last_name: Oliveto
- first_name: Tiago
full_name: Paixao, Tiago
id: 2C5658E6-F248-11E8-B48F-1D18A9856A87
last_name: Paixao
orcid: 0000-0003-2361-3953
- first_name: Jorge
full_name: Pérez Heredia, Jorge
last_name: Pérez Heredia
- first_name: Dirk
full_name: Sudholt, Dirk
last_name: Sudholt
- first_name: Barbora
full_name: Trubenova, Barbora
id: 42302D54-F248-11E8-B48F-1D18A9856A87
last_name: Trubenova
orcid: 0000-0002-6873-2967
citation:
ama: Oliveto P, Paixao T, Pérez Heredia J, Sudholt D, Trubenova B. How to escape
local optima in black box optimisation when non elitism outperforms elitism. Algorithmica.
2018;80(5):1604-1633. doi:10.1007/s00453-017-0369-2
apa: Oliveto, P., Paixao, T., Pérez Heredia, J., Sudholt, D., & Trubenova, B.
(2018). How to escape local optima in black box optimisation when non elitism
outperforms elitism. Algorithmica. Springer. https://doi.org/10.1007/s00453-017-0369-2
chicago: Oliveto, Pietro, Tiago Paixao, Jorge Pérez Heredia, Dirk Sudholt, and Barbora
Trubenova. “How to Escape Local Optima in Black Box Optimisation When Non Elitism
Outperforms Elitism.” Algorithmica. Springer, 2018. https://doi.org/10.1007/s00453-017-0369-2.
ieee: P. Oliveto, T. Paixao, J. Pérez Heredia, D. Sudholt, and B. Trubenova, “How
to escape local optima in black box optimisation when non elitism outperforms
elitism,” Algorithmica, vol. 80, no. 5. Springer, pp. 1604–1633, 2018.
ista: Oliveto P, Paixao T, Pérez Heredia J, Sudholt D, Trubenova B. 2018. How to
escape local optima in black box optimisation when non elitism outperforms elitism.
Algorithmica. 80(5), 1604–1633.
mla: Oliveto, Pietro, et al. “How to Escape Local Optima in Black Box Optimisation
When Non Elitism Outperforms Elitism.” Algorithmica, vol. 80, no. 5, Springer,
2018, pp. 1604–33, doi:10.1007/s00453-017-0369-2.
short: P. Oliveto, T. Paixao, J. Pérez Heredia, D. Sudholt, B. Trubenova, Algorithmica
80 (2018) 1604–1633.
date_created: 2018-12-11T11:48:09Z
date_published: 2018-05-01T00:00:00Z
date_updated: 2023-09-11T14:11:35Z
day: '01'
ddc:
- '576'
department:
- _id: NiBa
- _id: CaGu
doi: 10.1007/s00453-017-0369-2
ec_funded: 1
external_id:
isi:
- '000428239300010'
file:
- access_level: open_access
checksum: 7d92f5d7be81e387edeec4f06442791c
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:08:14Z
date_updated: 2020-07-14T12:47:54Z
file_id: '4674'
file_name: IST-2018-1014-v1+1_2018_Paixao_Escape.pdf
file_size: 691245
relation: main_file
file_date_updated: 2020-07-14T12:47:54Z
has_accepted_license: '1'
intvolume: ' 80'
isi: 1
issue: '5'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '05'
oa: 1
oa_version: Published Version
page: 1604 - 1633
project:
- _id: 25B1EC9E-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '618091'
name: Speed of Adaptation in Population Genetics and Evolutionary Computation
publication: Algorithmica
publication_status: published
publisher: Springer
publist_id: '6957'
pubrep_id: '1014'
quality_controlled: '1'
scopus_import: '1'
status: public
title: How to escape local optima in black box optimisation when non elitism outperforms
elitism
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 80
year: '2018'
...
---
_id: '1111'
abstract:
- lang: eng
text: Adaptation depends critically on the effects of new mutations and their dependency
on the genetic background in which they occur. These two factors can be summarized
by the fitness landscape. However, it would require testing all mutations in all
backgrounds, making the definition and analysis of fitness landscapes mostly inaccessible.
Instead of postulating a particular fitness landscape, we address this problem
by considering general classes of landscapes and calculating an upper limit for
the time it takes for a population to reach a fitness peak, circumventing the
need to have full knowledge about the fitness landscape. We analyze populations
in the weak-mutation regime and characterize the conditions that enable them to
quickly reach the fitness peak as a function of the number of sites under selection.
We show that for additive landscapes there is a critical selection strength enabling
populations to reach high-fitness genotypes, regardless of the distribution of
effects. This threshold scales with the number of sites under selection, effectively
setting a limit to adaptation, and results from the inevitable increase in deleterious
mutational pressure as the population adapts in a space of discrete genotypes.
Furthermore, we show that for the class of all unimodal landscapes this condition
is sufficient but not necessary for rapid adaptation, as in some highly epistatic
landscapes the critical strength does not depend on the number of sites under
selection; effectively removing this barrier to adaptation.
article_processing_charge: No
article_type: original
author:
- first_name: Jorge
full_name: Heredia, Jorge
last_name: Heredia
- first_name: Barbora
full_name: Trubenova, Barbora
id: 42302D54-F248-11E8-B48F-1D18A9856A87
last_name: Trubenova
orcid: 0000-0002-6873-2967
- first_name: Dirk
full_name: Sudholt, Dirk
last_name: Sudholt
- first_name: Tiago
full_name: Paixao, Tiago
id: 2C5658E6-F248-11E8-B48F-1D18A9856A87
last_name: Paixao
orcid: 0000-0003-2361-3953
citation:
ama: Heredia J, Trubenova B, Sudholt D, Paixao T. Selection limits to adaptive walks
on correlated landscapes. Genetics. 2017;205(2):803-825. doi:10.1534/genetics.116.189340
apa: Heredia, J., Trubenova, B., Sudholt, D., & Paixao, T. (2017). Selection
limits to adaptive walks on correlated landscapes. Genetics. Genetics Society
of America. https://doi.org/10.1534/genetics.116.189340
chicago: Heredia, Jorge, Barbora Trubenova, Dirk Sudholt, and Tiago Paixao. “Selection
Limits to Adaptive Walks on Correlated Landscapes.” Genetics. Genetics
Society of America, 2017. https://doi.org/10.1534/genetics.116.189340.
ieee: J. Heredia, B. Trubenova, D. Sudholt, and T. Paixao, “Selection limits to
adaptive walks on correlated landscapes,” Genetics, vol. 205, no. 2. Genetics
Society of America, pp. 803–825, 2017.
ista: Heredia J, Trubenova B, Sudholt D, Paixao T. 2017. Selection limits to adaptive
walks on correlated landscapes. Genetics. 205(2), 803–825.
mla: Heredia, Jorge, et al. “Selection Limits to Adaptive Walks on Correlated Landscapes.”
Genetics, vol. 205, no. 2, Genetics Society of America, 2017, pp. 803–25,
doi:10.1534/genetics.116.189340.
short: J. Heredia, B. Trubenova, D. Sudholt, T. Paixao, Genetics 205 (2017) 803–825.
date_created: 2018-12-11T11:50:12Z
date_published: 2017-02-01T00:00:00Z
date_updated: 2023-09-20T11:35:03Z
day: '01'
department:
- _id: NiBa
doi: 10.1534/genetics.116.189340
ec_funded: 1
external_id:
isi:
- '000394144900025'
pmid:
- '27881471'
intvolume: ' 205'
isi: 1
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1534/genetics.116.189340
month: '02'
oa: 1
oa_version: Published Version
page: 803 - 825
pmid: 1
project:
- _id: 25B1EC9E-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '618091'
name: Speed of Adaptation in Population Genetics and Evolutionary Computation
publication: Genetics
publication_identifier:
issn:
- '00166731'
publication_status: published
publisher: Genetics Society of America
publist_id: '6256'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Selection limits to adaptive walks on correlated landscapes
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 205
year: '2017'
...
---
_id: '1169'
abstract:
- lang: eng
text: Dispersal is a crucial factor in natural evolution, since it determines the
habitat experienced by any population and defines the spatial scale of interactions
between individuals. There is compelling evidence for systematic differences in
dispersal characteristics within the same population, i.e., genotype-dependent
dispersal. The consequences of genotype-dependent dispersal on other evolutionary
phenomena, however, are poorly understood. In this article we investigate the
effect of genotype-dependent dispersal on spatial gene frequency patterns, using
a generalization of the classical diffusion model of selection and dispersal.
Dispersal is characterized by the variance of dispersal (diffusion coefficient)
and the mean displacement (directional advection term). We demonstrate that genotype-dependent
dispersal may change the qualitative behavior of Fisher waves, which change from
being “pulled” to being “pushed” wave fronts as the discrepancy in dispersal between
genotypes increases. The speed of any wave is partitioned into components due
to selection, genotype-dependent variance of dispersal, and genotype-dependent
mean displacement. We apply our findings to wave fronts maintained by selection
against heterozygotes. Furthermore, we identify a benefit of increased variance
of dispersal, quantify its effect on the speed of the wave, and discuss the implications
for the evolution of dispersal strategies.
article_processing_charge: No
author:
- first_name: Sebastian
full_name: Novak, Sebastian
id: 461468AE-F248-11E8-B48F-1D18A9856A87
last_name: Novak
orcid: 0000-0002-2519-824X
- first_name: Richard
full_name: Kollár, Richard
last_name: Kollár
citation:
ama: Novak S, Kollár R. Spatial gene frequency waves under genotype dependent dispersal.
Genetics. 2017;205(1):367-374. doi:10.1534/genetics.116.193946
apa: Novak, S., & Kollár, R. (2017). Spatial gene frequency waves under genotype
dependent dispersal. Genetics. Genetics Society of America. https://doi.org/10.1534/genetics.116.193946
chicago: Novak, Sebastian, and Richard Kollár. “Spatial Gene Frequency Waves under
Genotype Dependent Dispersal.” Genetics. Genetics Society of America, 2017.
https://doi.org/10.1534/genetics.116.193946.
ieee: S. Novak and R. Kollár, “Spatial gene frequency waves under genotype dependent
dispersal,” Genetics, vol. 205, no. 1. Genetics Society of America, pp.
367–374, 2017.
ista: Novak S, Kollár R. 2017. Spatial gene frequency waves under genotype dependent
dispersal. Genetics. 205(1), 367–374.
mla: Novak, Sebastian, and Richard Kollár. “Spatial Gene Frequency Waves under Genotype
Dependent Dispersal.” Genetics, vol. 205, no. 1, Genetics Society of America,
2017, pp. 367–74, doi:10.1534/genetics.116.193946.
short: S. Novak, R. Kollár, Genetics 205 (2017) 367–374.
date_created: 2018-12-11T11:50:31Z
date_published: 2017-01-01T00:00:00Z
date_updated: 2025-05-28T11:42:46Z
day: '01'
ddc:
- '576'
department:
- _id: NiBa
doi: 10.1534/genetics.116.193946
ec_funded: 1
external_id:
isi:
- '000393677300025'
file:
- access_level: open_access
checksum: 7c8ab79cda1f92760bbbbe0f53175bfc
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:10:43Z
date_updated: 2020-07-14T12:44:37Z
file_id: '4833'
file_name: IST-2016-727-v1+1_SFC_Genetics_final.pdf
file_size: 361500
relation: main_file
file_date_updated: 2020-07-14T12:44:37Z
has_accepted_license: '1'
intvolume: ' 205'
isi: 1
issue: '1'
language:
- iso: eng
month: '01'
oa: 1
oa_version: Submitted Version
page: 367 - 374
project:
- _id: 25B1EC9E-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '618091'
name: Speed of Adaptation in Population Genetics and Evolutionary Computation
- _id: 25B07788-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '250152'
name: Limits to selection in biology and in evolutionary computation
publication: Genetics
publication_identifier:
issn:
- '00166731'
publication_status: published
publisher: Genetics Society of America
publist_id: '6188'
pubrep_id: '727'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Spatial gene frequency waves under genotype dependent dispersal
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 205
year: '2017'
...
---
_id: '696'
abstract:
- lang: eng
text: Mutator strains are expected to evolve when the availability and effect of
beneficial mutations are high enough to counteract the disadvantage from deleterious
mutations that will inevitably accumulate. As the population becomes more adapted
to its environment, both availability and effect of beneficial mutations necessarily
decrease and mutation rates are predicted to decrease. It has been shown that
certain molecular mechanisms can lead to increased mutation rates when the organism
finds itself in a stressful environment. While this may be a correlated response
to other functions, it could also be an adaptive mechanism, raising mutation rates
only when it is most advantageous. Here, we use a mathematical model to investigate
the plausibility of the adaptive hypothesis. We show that such a mechanism can
be mantained if the population is subjected to diverse stresses. By simulating
various antibiotic treatment schemes, we find that combination treatments can
reduce the effectiveness of second-order selection on stress-induced mutagenesis.
We discuss the implications of our results to strategies of antibiotic therapy.
article_number: e1005609
article_type: original
author:
- first_name: Marta
full_name: Lukacisinova, Marta
id: 4342E402-F248-11E8-B48F-1D18A9856A87
last_name: Lukacisinova
orcid: 0000-0002-2519-8004
- first_name: Sebastian
full_name: Novak, Sebastian
id: 461468AE-F248-11E8-B48F-1D18A9856A87
last_name: Novak
orcid: 0000-0002-2519-824X
- first_name: Tiago
full_name: Paixao, Tiago
id: 2C5658E6-F248-11E8-B48F-1D18A9856A87
last_name: Paixao
orcid: 0000-0003-2361-3953
citation:
ama: 'Lukacisinova M, Novak S, Paixao T. Stress induced mutagenesis: Stress diversity
facilitates the persistence of mutator genes. PLoS Computational Biology.
2017;13(7). doi:10.1371/journal.pcbi.1005609'
apa: 'Lukacisinova, M., Novak, S., & Paixao, T. (2017). Stress induced mutagenesis:
Stress diversity facilitates the persistence of mutator genes. PLoS Computational
Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005609'
chicago: 'Lukacisinova, Marta, Sebastian Novak, and Tiago Paixao. “Stress Induced
Mutagenesis: Stress Diversity Facilitates the Persistence of Mutator Genes.” PLoS
Computational Biology. Public Library of Science, 2017. https://doi.org/10.1371/journal.pcbi.1005609.'
ieee: 'M. Lukacisinova, S. Novak, and T. Paixao, “Stress induced mutagenesis: Stress
diversity facilitates the persistence of mutator genes,” PLoS Computational
Biology, vol. 13, no. 7. Public Library of Science, 2017.'
ista: 'Lukacisinova M, Novak S, Paixao T. 2017. Stress induced mutagenesis: Stress
diversity facilitates the persistence of mutator genes. PLoS Computational Biology.
13(7), e1005609.'
mla: 'Lukacisinova, Marta, et al. “Stress Induced Mutagenesis: Stress Diversity
Facilitates the Persistence of Mutator Genes.” PLoS Computational Biology,
vol. 13, no. 7, e1005609, Public Library of Science, 2017, doi:10.1371/journal.pcbi.1005609.'
short: M. Lukacisinova, S. Novak, T. Paixao, PLoS Computational Biology 13 (2017).
date_created: 2018-12-11T11:47:58Z
date_published: 2017-07-18T00:00:00Z
date_updated: 2024-03-25T23:30:14Z
day: '18'
ddc:
- '576'
department:
- _id: ToBo
- _id: NiBa
- _id: CaGu
doi: 10.1371/journal.pcbi.1005609
ec_funded: 1
file:
- access_level: open_access
checksum: 9143c290fa6458ed2563bff4b295554a
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:15:01Z
date_updated: 2020-07-14T12:47:46Z
file_id: '5117'
file_name: IST-2017-894-v1+1_journal.pcbi.1005609.pdf
file_size: 3775716
relation: main_file
file_date_updated: 2020-07-14T12:47:46Z
has_accepted_license: '1'
intvolume: ' 13'
issue: '7'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
project:
- _id: 25B1EC9E-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '618091'
name: Speed of Adaptation in Population Genetics and Evolutionary Computation
publication: PLoS Computational Biology
publication_identifier:
issn:
- 1553734X
publication_status: published
publisher: Public Library of Science
publist_id: '7004'
pubrep_id: '894'
quality_controlled: '1'
related_material:
record:
- id: '9849'
relation: research_data
status: public
- id: '9850'
relation: research_data
status: public
- id: '9851'
relation: research_data
status: public
- id: '9852'
relation: research_data
status: public
- id: '6263'
relation: dissertation_contains
status: public
scopus_import: 1
status: public
title: 'Stress induced mutagenesis: Stress diversity facilitates the persistence of
mutator genes'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 13
year: '2017'
...
---
_id: '1336'
abstract:
- lang: eng
text: Evolutionary algorithms (EAs) form a popular optimisation paradigm inspired
by natural evolution. In recent years the field of evolutionary computation has
developed a rigorous analytical theory to analyse the runtimes of EAs on many
illustrative problems. Here we apply this theory to a simple model of natural
evolution. In the Strong Selection Weak Mutation (SSWM) evolutionary regime the
time between occurrences of new mutations is much longer than the time it takes
for a mutated genotype to take over the population. In this situation, the population
only contains copies of one genotype and evolution can be modelled as a stochastic
process evolving one genotype by means of mutation and selection between the resident
and the mutated genotype. The probability of accepting the mutated genotype then
depends on the change in fitness. We study this process, SSWM, from an algorithmic
perspective, quantifying its expected optimisation time for various parameters
and investigating differences to a similar evolutionary algorithm, the well-known
(1+1) EA. We show that SSWM can have a moderate advantage over the (1+1) EA at
crossing fitness valleys and study an example where SSWM outperforms the (1+1)
EA by taking advantage of information on the fitness gradient.
article_processing_charge: No
author:
- first_name: Tiago
full_name: Paixao, Tiago
id: 2C5658E6-F248-11E8-B48F-1D18A9856A87
last_name: Paixao
orcid: 0000-0003-2361-3953
- first_name: Jorge
full_name: Pérez Heredia, Jorge
last_name: Pérez Heredia
- first_name: Dirk
full_name: Sudholt, Dirk
last_name: Sudholt
- first_name: Barbora
full_name: Trubenova, Barbora
id: 42302D54-F248-11E8-B48F-1D18A9856A87
last_name: Trubenova
orcid: 0000-0002-6873-2967
citation:
ama: Paixao T, Pérez Heredia J, Sudholt D, Trubenova B. Towards a runtime comparison
of natural and artificial evolution. Algorithmica. 2017;78(2):681-713.
doi:10.1007/s00453-016-0212-1
apa: Paixao, T., Pérez Heredia, J., Sudholt, D., & Trubenova, B. (2017). Towards
a runtime comparison of natural and artificial evolution. Algorithmica.
Springer. https://doi.org/10.1007/s00453-016-0212-1
chicago: Paixao, Tiago, Jorge Pérez Heredia, Dirk Sudholt, and Barbora Trubenova.
“Towards a Runtime Comparison of Natural and Artificial Evolution.” Algorithmica.
Springer, 2017. https://doi.org/10.1007/s00453-016-0212-1.
ieee: T. Paixao, J. Pérez Heredia, D. Sudholt, and B. Trubenova, “Towards a runtime
comparison of natural and artificial evolution,” Algorithmica, vol. 78,
no. 2. Springer, pp. 681–713, 2017.
ista: Paixao T, Pérez Heredia J, Sudholt D, Trubenova B. 2017. Towards a runtime
comparison of natural and artificial evolution. Algorithmica. 78(2), 681–713.
mla: Paixao, Tiago, et al. “Towards a Runtime Comparison of Natural and Artificial
Evolution.” Algorithmica, vol. 78, no. 2, Springer, 2017, pp. 681–713,
doi:10.1007/s00453-016-0212-1.
short: T. Paixao, J. Pérez Heredia, D. Sudholt, B. Trubenova, Algorithmica 78 (2017)
681–713.
date_created: 2018-12-11T11:51:27Z
date_published: 2017-06-01T00:00:00Z
date_updated: 2023-09-20T11:14:42Z
day: '01'
ddc:
- '576'
department:
- _id: NiBa
- _id: CaGu
doi: 10.1007/s00453-016-0212-1
ec_funded: 1
external_id:
isi:
- '000400379500013'
file:
- access_level: open_access
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creator: system
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oa_version: Published Version
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call_identifier: FP7
grant_number: '618091'
name: Speed of Adaptation in Population Genetics and Evolutionary Computation
publication: Algorithmica
publication_identifier:
issn:
- '01784617'
publication_status: published
publisher: Springer
publist_id: '5931'
pubrep_id: '658'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Towards a runtime comparison of natural and artificial evolution
tmp:
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legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 78
year: '2017'
...
---
_id: '1351'
abstract:
- lang: eng
text: The behaviour of gene regulatory networks (GRNs) is typically analysed using
simulation-based statistical testing-like methods. In this paper, we demonstrate
that we can replace this approach by a formal verification-like method that gives
higher assurance and scalability. We focus on Wagner’s weighted GRN model with
varying weights, which is used in evolutionary biology. In the model, weight parameters
represent the gene interaction strength that may change due to genetic mutations.
For a property of interest, we synthesise the constraints over the parameter space
that represent the set of GRNs satisfying the property. We experimentally show
that our parameter synthesis procedure computes the mutational robustness of GRNs—an
important problem of interest in evolutionary biology—more efficiently than the
classical simulation method. We specify the property in linear temporal logic.
We employ symbolic bounded model checking and SMT solving to compute the space
of GRNs that satisfy the property, which amounts to synthesizing a set of linear
constraints on the weights.
article_processing_charge: No
author:
- first_name: Mirco
full_name: Giacobbe, Mirco
id: 3444EA5E-F248-11E8-B48F-1D18A9856A87
last_name: Giacobbe
orcid: 0000-0001-8180-0904
- first_name: Calin C
full_name: Guet, Calin C
id: 47F8433E-F248-11E8-B48F-1D18A9856A87
last_name: Guet
orcid: 0000-0001-6220-2052
- first_name: Ashutosh
full_name: Gupta, Ashutosh
id: 335E5684-F248-11E8-B48F-1D18A9856A87
last_name: Gupta
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000−0002−2985−7724
- first_name: Tiago
full_name: Paixao, Tiago
id: 2C5658E6-F248-11E8-B48F-1D18A9856A87
last_name: Paixao
orcid: 0000-0003-2361-3953
- first_name: Tatjana
full_name: Petrov, Tatjana
id: 3D5811FC-F248-11E8-B48F-1D18A9856A87
last_name: Petrov
orcid: 0000-0002-9041-0905
citation:
ama: Giacobbe M, Guet CC, Gupta A, Henzinger TA, Paixao T, Petrov T. Model checking
the evolution of gene regulatory networks. Acta Informatica. 2017;54(8):765-787.
doi:10.1007/s00236-016-0278-x
apa: Giacobbe, M., Guet, C. C., Gupta, A., Henzinger, T. A., Paixao, T., & Petrov,
T. (2017). Model checking the evolution of gene regulatory networks. Acta Informatica.
Springer. https://doi.org/10.1007/s00236-016-0278-x
chicago: Giacobbe, Mirco, Calin C Guet, Ashutosh Gupta, Thomas A Henzinger, Tiago
Paixao, and Tatjana Petrov. “Model Checking the Evolution of Gene Regulatory Networks.”
Acta Informatica. Springer, 2017. https://doi.org/10.1007/s00236-016-0278-x.
ieee: M. Giacobbe, C. C. Guet, A. Gupta, T. A. Henzinger, T. Paixao, and T. Petrov,
“Model checking the evolution of gene regulatory networks,” Acta Informatica,
vol. 54, no. 8. Springer, pp. 765–787, 2017.
ista: Giacobbe M, Guet CC, Gupta A, Henzinger TA, Paixao T, Petrov T. 2017. Model
checking the evolution of gene regulatory networks. Acta Informatica. 54(8), 765–787.
mla: Giacobbe, Mirco, et al. “Model Checking the Evolution of Gene Regulatory Networks.”
Acta Informatica, vol. 54, no. 8, Springer, 2017, pp. 765–87, doi:10.1007/s00236-016-0278-x.
short: M. Giacobbe, C.C. Guet, A. Gupta, T.A. Henzinger, T. Paixao, T. Petrov, Acta
Informatica 54 (2017) 765–787.
date_created: 2018-12-11T11:51:32Z
date_published: 2017-12-01T00:00:00Z
date_updated: 2025-05-28T11:57:04Z
day: '01'
ddc:
- '006'
- '576'
department:
- _id: ToHe
- _id: CaGu
- _id: NiBa
doi: 10.1007/s00236-016-0278-x
ec_funded: 1
external_id:
isi:
- '000414343200003'
file:
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checksum: 4e661d9135d7f8c342e8e258dee76f3e
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creator: dernst
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month: '12'
oa: 1
oa_version: Published Version
page: 765 - 787
project:
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call_identifier: FP7
grant_number: '267989'
name: Quantitative Reactive Modeling
- _id: 25832EC2-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: S 11407_N23
name: Rigorous Systems Engineering
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
- _id: 25B1EC9E-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '618091'
name: Speed of Adaptation in Population Genetics and Evolutionary Computation
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
- _id: 25B07788-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '250152'
name: Limits to selection in biology and in evolutionary computation
publication: Acta Informatica
publication_identifier:
issn:
- '00015903'
publication_status: published
publisher: Springer
publist_id: '5898'
pubrep_id: '649'
quality_controlled: '1'
related_material:
record:
- id: '1835'
relation: earlier_version
status: public
scopus_import: '1'
status: public
title: Model checking the evolution of gene regulatory networks
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 54
year: '2017'
...
---
_id: '910'
abstract:
- lang: eng
text: "Frequency-independent selection is generally considered as a force that acts
to reduce the genetic variation in evolving populations, yet rigorous arguments
for this idea are scarce. When selection fluctuates in time, it is unclear whether
frequency-independent selection may maintain genetic polymorphism without invoking
additional mechanisms. We show that constant frequency-independent selection with
arbitrary epistasis on a well-mixed haploid population eliminates genetic variation
if we assume linkage equilibrium between alleles. To this end, we introduce the
notion of frequency-independent selection at the level of alleles, which is sufficient
to prove our claim and contains the notion of frequency-independent selection
on haploids. When selection and recombination are weak but of the same order,
there may be strong linkage disequilibrium; numerical calculations show that stable
equilibria are highly unlikely. Using the example of a diallelic two-locus model,
we then demonstrate that frequency-independent selection that fluctuates in time
can maintain stable polymorphism if linkage disequilibrium changes its sign periodically.
We put our findings in the context of results from the existing literature and
point out those scenarios in which the possible role of frequency-independent
selection in maintaining genetic variation remains unclear.\r\n"
article_processing_charge: No
author:
- first_name: Sebastian
full_name: Novak, Sebastian
id: 461468AE-F248-11E8-B48F-1D18A9856A87
last_name: Novak
orcid: 0000-0002-2519-824X
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
citation:
ama: Novak S, Barton NH. When does frequency-independent selection maintain genetic
variation? Genetics. 2017;207(2):653-668. doi:10.1534/genetics.117.300129
apa: Novak, S., & Barton, N. H. (2017). When does frequency-independent selection
maintain genetic variation? Genetics. Genetics Society of America. https://doi.org/10.1534/genetics.117.300129
chicago: Novak, Sebastian, and Nicholas H Barton. “When Does Frequency-Independent
Selection Maintain Genetic Variation?” Genetics. Genetics Society of America,
2017. https://doi.org/10.1534/genetics.117.300129.
ieee: S. Novak and N. H. Barton, “When does frequency-independent selection maintain
genetic variation?,” Genetics, vol. 207, no. 2. Genetics Society of America,
pp. 653–668, 2017.
ista: Novak S, Barton NH. 2017. When does frequency-independent selection maintain
genetic variation? Genetics. 207(2), 653–668.
mla: Novak, Sebastian, and Nicholas H. Barton. “When Does Frequency-Independent
Selection Maintain Genetic Variation?” Genetics, vol. 207, no. 2, Genetics
Society of America, 2017, pp. 653–68, doi:10.1534/genetics.117.300129.
short: S. Novak, N.H. Barton, Genetics 207 (2017) 653–668.
date_created: 2018-12-11T11:49:09Z
date_published: 2017-10-01T00:00:00Z
date_updated: 2023-09-26T15:49:15Z
day: '01'
ddc:
- '576'
department:
- _id: NiBa
doi: 10.1534/genetics.117.300129
ec_funded: 1
external_id:
isi:
- '000412232600019'
file:
- access_level: open_access
checksum: f7c32dabf52e6d9e709d9203761e39fd
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:17:12Z
date_updated: 2020-07-14T12:48:15Z
file_id: '5264'
file_name: IST-2018-974-v1+1_manuscript.pdf
file_size: 494268
relation: main_file
file_date_updated: 2020-07-14T12:48:15Z
has_accepted_license: '1'
intvolume: ' 207'
isi: 1
issue: '2'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Submitted Version
page: 653 - 668
project:
- _id: 25B1EC9E-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '618091'
name: Speed of Adaptation in Population Genetics and Evolutionary Computation
publication: Genetics
publication_status: published
publisher: Genetics Society of America
publist_id: '6533'
pubrep_id: '974'
quality_controlled: '1'
scopus_import: '1'
status: public
title: When does frequency-independent selection maintain genetic variation?
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 207
year: '2017'
...
---
_id: '954'
abstract:
- lang: eng
text: Understanding the relation between genotype and phenotype remains a major
challenge. The difficulty of predicting individual mutation effects, and particularly
the interactions between them, has prevented the development of a comprehensive
theory that links genotypic changes to their phenotypic effects. We show that
a general thermodynamic framework for gene regulation, based on a biophysical
understanding of protein-DNA binding, accurately predicts the sign of epistasis
in a canonical cis-regulatory element consisting of overlapping RNA polymerase
and repressor binding sites. Sign and magnitude of individual mutation effects
are sufficient to predict the sign of epistasis and its environmental dependence.
Thus, the thermodynamic model offers the correct null prediction for epistasis
between mutations across DNA-binding sites. Our results indicate that a predictive
theory for the effects of cis-regulatory mutations is possible from first principles,
as long as the essential molecular mechanisms and the constraints these impose
on a biological system are accounted for.
article_number: e25192
article_processing_charge: Yes
author:
- first_name: Mato
full_name: Lagator, Mato
id: 345D25EC-F248-11E8-B48F-1D18A9856A87
last_name: Lagator
- first_name: Tiago
full_name: Paixao, Tiago
id: 2C5658E6-F248-11E8-B48F-1D18A9856A87
last_name: Paixao
orcid: 0000-0003-2361-3953
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
- first_name: Jonathan P
full_name: Bollback, Jonathan P
id: 2C6FA9CC-F248-11E8-B48F-1D18A9856A87
last_name: Bollback
orcid: 0000-0002-4624-4612
- first_name: Calin C
full_name: Guet, Calin C
id: 47F8433E-F248-11E8-B48F-1D18A9856A87
last_name: Guet
orcid: 0000-0001-6220-2052
citation:
ama: Lagator M, Paixao T, Barton NH, Bollback JP, Guet CC. On the mechanistic nature
of epistasis in a canonical cis-regulatory element. eLife. 2017;6. doi:10.7554/eLife.25192
apa: Lagator, M., Paixao, T., Barton, N. H., Bollback, J. P., & Guet, C. C.
(2017). On the mechanistic nature of epistasis in a canonical cis-regulatory element.
ELife. eLife Sciences Publications. https://doi.org/10.7554/eLife.25192
chicago: Lagator, Mato, Tiago Paixao, Nicholas H Barton, Jonathan P Bollback, and
Calin C Guet. “On the Mechanistic Nature of Epistasis in a Canonical Cis-Regulatory
Element.” ELife. eLife Sciences Publications, 2017. https://doi.org/10.7554/eLife.25192.
ieee: M. Lagator, T. Paixao, N. H. Barton, J. P. Bollback, and C. C. Guet, “On the
mechanistic nature of epistasis in a canonical cis-regulatory element,” eLife,
vol. 6. eLife Sciences Publications, 2017.
ista: Lagator M, Paixao T, Barton NH, Bollback JP, Guet CC. 2017. On the mechanistic
nature of epistasis in a canonical cis-regulatory element. eLife. 6, e25192.
mla: Lagator, Mato, et al. “On the Mechanistic Nature of Epistasis in a Canonical
Cis-Regulatory Element.” ELife, vol. 6, e25192, eLife Sciences Publications,
2017, doi:10.7554/eLife.25192.
short: M. Lagator, T. Paixao, N.H. Barton, J.P. Bollback, C.C. Guet, ELife 6 (2017).
date_created: 2018-12-11T11:49:23Z
date_published: 2017-05-18T00:00:00Z
date_updated: 2023-09-22T10:01:17Z
day: '18'
ddc:
- '576'
department:
- _id: CaGu
- _id: NiBa
- _id: JoBo
doi: 10.7554/eLife.25192
ec_funded: 1
external_id:
isi:
- '000404024800001'
file:
- access_level: open_access
checksum: 59cdd4400fb41280122d414fea971546
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:17:49Z
date_updated: 2020-07-14T12:48:16Z
file_id: '5306'
file_name: IST-2017-841-v1+1_elife-25192-v2.pdf
file_size: 2441529
relation: main_file
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checksum: b69024880558b858eb8c5d47a92b6377
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:17:50Z
date_updated: 2020-07-14T12:48:16Z
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file_size: 3752660
relation: main_file
file_date_updated: 2020-07-14T12:48:16Z
has_accepted_license: '1'
intvolume: ' 6'
isi: 1
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
project:
- _id: 25B1EC9E-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '618091'
name: Speed of Adaptation in Population Genetics and Evolutionary Computation
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
- _id: 2578D616-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '648440'
name: Selective Barriers to Horizontal Gene Transfer
publication: eLife
publication_identifier:
issn:
- 2050084X
publication_status: published
publisher: eLife Sciences Publications
publist_id: '6460'
pubrep_id: '841'
quality_controlled: '1'
scopus_import: '1'
status: public
title: On the mechanistic nature of epistasis in a canonical cis-regulatory element
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 6
year: '2017'
...
---
_id: '1191'
abstract:
- lang: eng
text: Variation in genotypes may be responsible for differences in dispersal rates,
directional biases, and growth rates of individuals. These traits may favor certain
genotypes and enhance their spatiotemporal spreading into areas occupied by the
less advantageous genotypes. We study how these factors influence the speed of
spreading in the case of two competing genotypes under the assumption that spatial
variation of the total population is small compared to the spatial variation of
the frequencies of the genotypes in the population. In that case, the dynamics
of the frequency of one of the genotypes is approximately described by a generalized
Fisher–Kolmogorov–Petrovskii–Piskunov (F–KPP) equation. This generalized F–KPP
equation with (nonlinear) frequency-dependent diffusion and advection terms admits
traveling wave solutions that characterize the invasion of the dominant genotype.
Our existence results generalize the classical theory for traveling waves for
the F–KPP with constant coefficients. Moreover, in the particular case of the
quadratic (monostable) nonlinear growth–decay rate in the generalized F–KPP we
study in detail the influence of the variance in diffusion and mean displacement
rates of the two genotypes on the minimal wave propagation speed.
acknowledgement: "We thank Nick Barton, Katarína Bod’ová, and Sr\r\n-\r\ndan Sarikas
for constructive feed-\r\nback and support. Furthermore, we would like to express
our deep gratitude to the anonymous referees (one\r\nof whom, Jimmy Garnier, agreed
to reveal his identity) and the editor Max Souza, for very helpful and\r\ndetailed
comments and suggestions that significantly helped us to improve the manuscript.
This project has\r\nreceived funding from the European Union’s Seventh Framework
Programme for research, technological\r\ndevelopment and demonstration under Grant
Agreement 618091 Speed of Adaptation in Population Genet-\r\nics and Evolutionary
Computation (SAGE) and the European Research Council (ERC) Grant No. 250152\r\n(SN),
from the Scientific Grant Agency of the Slovak Republic under the Grant 1/0459/13
and by the Slovak\r\nResearch and Development Agency under the Contract No. APVV-14-0378
(RK). RK would also like to\r\nthank IST Austria for its hospitality during the
work on this project."
author:
- first_name: Richard
full_name: Kollár, Richard
last_name: Kollár
- first_name: Sebastian
full_name: Novak, Sebastian
id: 461468AE-F248-11E8-B48F-1D18A9856A87
last_name: Novak
orcid: 0000-0002-2519-824X
citation:
ama: Kollár R, Novak S. Existence of traveling waves for the generalized F–KPP equation.
Bulletin of Mathematical Biology. 2017;79(3):525-559. doi:10.1007/s11538-016-0244-3
apa: Kollár, R., & Novak, S. (2017). Existence of traveling waves for the generalized
F–KPP equation. Bulletin of Mathematical Biology. Springer. https://doi.org/10.1007/s11538-016-0244-3
chicago: Kollár, Richard, and Sebastian Novak. “Existence of Traveling Waves for
the Generalized F–KPP Equation.” Bulletin of Mathematical Biology. Springer,
2017. https://doi.org/10.1007/s11538-016-0244-3.
ieee: R. Kollár and S. Novak, “Existence of traveling waves for the generalized
F–KPP equation,” Bulletin of Mathematical Biology, vol. 79, no. 3. Springer,
pp. 525–559, 2017.
ista: Kollár R, Novak S. 2017. Existence of traveling waves for the generalized
F–KPP equation. Bulletin of Mathematical Biology. 79(3), 525–559.
mla: Kollár, Richard, and Sebastian Novak. “Existence of Traveling Waves for the
Generalized F–KPP Equation.” Bulletin of Mathematical Biology, vol. 79,
no. 3, Springer, 2017, pp. 525–59, doi:10.1007/s11538-016-0244-3.
short: R. Kollár, S. Novak, Bulletin of Mathematical Biology 79 (2017) 525–559.
date_created: 2018-12-11T11:50:38Z
date_published: 2017-03-01T00:00:00Z
date_updated: 2025-05-28T11:42:46Z
day: '01'
department:
- _id: NiBa
doi: 10.1007/s11538-016-0244-3
ec_funded: 1
intvolume: ' 79'
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1607.00944
month: '03'
oa: 1
oa_version: Preprint
page: 525-559
project:
- _id: 25B1EC9E-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '618091'
name: Speed of Adaptation in Population Genetics and Evolutionary Computation
- _id: 25B07788-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '250152'
name: Limits to selection in biology and in evolutionary computation
publication: Bulletin of Mathematical Biology
publication_status: published
publisher: Springer
publist_id: '6160'
quality_controlled: '1'
scopus_import: 1
status: public
title: Existence of traveling waves for the generalized F–KPP equation
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 79
year: '2017'
...
---
_id: '1349'
abstract:
- lang: eng
text: Crossing fitness valleys is one of the major obstacles to function optimization.
In this paper we investigate how the structure of the fitness valley, namely its
depth d and length ℓ, influence the runtime of different strategies for crossing
these valleys. We present a runtime comparison between the (1+1) EA and two non-elitist
nature-inspired algorithms, Strong Selection Weak Mutation (SSWM) and the Metropolis
algorithm. While the (1+1) EA has to jump across the valley to a point of higher
fitness because it does not accept decreasing moves, the non-elitist algorithms
may cross the valley by accepting worsening moves. We show that while the runtime
of the (1+1) EA algorithm depends critically on the length of the valley, the
runtimes of the non-elitist algorithms depend crucially only on the depth of the
valley. In particular, the expected runtime of both SSWM and Metropolis is polynomial
in ℓ and exponential in d while the (1+1) EA is efficient only for valleys of
small length. Moreover, we show that both SSWM and Metropolis can also efficiently
optimize a rugged function consisting of consecutive valleys.
author:
- first_name: Pietro
full_name: Oliveto, Pietro
last_name: Oliveto
- first_name: Tiago
full_name: Paixao, Tiago
id: 2C5658E6-F248-11E8-B48F-1D18A9856A87
last_name: Paixao
orcid: 0000-0003-2361-3953
- first_name: Jorge
full_name: Heredia, Jorge
last_name: Heredia
- first_name: Dirk
full_name: Sudholt, Dirk
last_name: Sudholt
- first_name: Barbora
full_name: Trubenova, Barbora
id: 42302D54-F248-11E8-B48F-1D18A9856A87
last_name: Trubenova
orcid: 0000-0002-6873-2967
citation:
ama: 'Oliveto P, Paixao T, Heredia J, Sudholt D, Trubenova B. When non-elitism outperforms
elitism for crossing fitness valleys. In: Proceedings of the Genetic and Evolutionary
Computation Conference 2016 . ACM; 2016:1163-1170. doi:10.1145/2908812.2908909'
apa: 'Oliveto, P., Paixao, T., Heredia, J., Sudholt, D., & Trubenova, B. (2016).
When non-elitism outperforms elitism for crossing fitness valleys. In Proceedings
of the Genetic and Evolutionary Computation Conference 2016 (pp. 1163–1170).
Denver, CO, USA: ACM. https://doi.org/10.1145/2908812.2908909'
chicago: Oliveto, Pietro, Tiago Paixao, Jorge Heredia, Dirk Sudholt, and Barbora
Trubenova. “When Non-Elitism Outperforms Elitism for Crossing Fitness Valleys.”
In Proceedings of the Genetic and Evolutionary Computation Conference 2016
, 1163–70. ACM, 2016. https://doi.org/10.1145/2908812.2908909.
ieee: P. Oliveto, T. Paixao, J. Heredia, D. Sudholt, and B. Trubenova, “When non-elitism
outperforms elitism for crossing fitness valleys,” in Proceedings of the Genetic
and Evolutionary Computation Conference 2016 , Denver, CO, USA, 2016, pp.
1163–1170.
ista: 'Oliveto P, Paixao T, Heredia J, Sudholt D, Trubenova B. 2016. When non-elitism
outperforms elitism for crossing fitness valleys. Proceedings of the Genetic and
Evolutionary Computation Conference 2016 . GECCO: Genetic and evolutionary computation
conference, 1163–1170.'
mla: Oliveto, Pietro, et al. “When Non-Elitism Outperforms Elitism for Crossing
Fitness Valleys.” Proceedings of the Genetic and Evolutionary Computation Conference
2016 , ACM, 2016, pp. 1163–70, doi:10.1145/2908812.2908909.
short: P. Oliveto, T. Paixao, J. Heredia, D. Sudholt, B. Trubenova, in:, Proceedings
of the Genetic and Evolutionary Computation Conference 2016 , ACM, 2016, pp. 1163–1170.
conference:
end_date: 2016-07-24
location: Denver, CO, USA
name: 'GECCO: Genetic and evolutionary computation conference'
start_date: 2016-07-20
date_created: 2018-12-11T11:51:31Z
date_published: 2016-07-20T00:00:00Z
date_updated: 2021-01-12T06:50:03Z
day: '20'
ddc:
- '576'
department:
- _id: NiBa
- _id: CaGu
doi: 10.1145/2908812.2908909
ec_funded: 1
file:
- access_level: open_access
checksum: a1896e39e4113f2711e46b435d5f3e69
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:16:27Z
date_updated: 2020-07-14T12:44:45Z
file_id: '5214'
file_name: IST-2016-650-v1+1_p1163-oliveto.pdf
file_size: 979026
relation: main_file
file_date_updated: 2020-07-14T12:44:45Z
has_accepted_license: '1'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: 1163 - 1170
project:
- _id: 25B1EC9E-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '618091'
name: Speed of Adaptation in Population Genetics and Evolutionary Computation
publication: 'Proceedings of the Genetic and Evolutionary Computation Conference 2016 '
publication_status: published
publisher: ACM
publist_id: '5900'
pubrep_id: '650'
quality_controlled: '1'
scopus_import: 1
status: public
title: When non-elitism outperforms elitism for crossing fitness valleys
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2016'
...
---
_id: '1359'
abstract:
- lang: eng
text: "The role of gene interactions in the evolutionary process has long\r\nbeen
controversial. Although some argue that they are not of\r\nimportance, because
most variation is additive, others claim that\r\ntheir effect in the long term
can be substantial. Here, we focus on\r\nthe long-term effects of genetic interactions
under directional\r\nselection assuming no mutation or dominance, and that epistasis
is\r\nsymmetrical overall. We ask by how much the mean of a complex\r\ntrait can
be increased by selection and analyze two extreme\r\nregimes, in which either
drift or selection dominate the dynamics\r\nof allele frequencies. In both scenarios,
epistatic interactions affect\r\nthe long-term response to selection by modulating
the additive\r\ngenetic variance. When drift dominates, we extend Robertson\r\n’\r\ns\r\n[Robertson
A (1960)\r\nProc R Soc Lond B Biol Sci\r\n153(951):234\r\n−\r\n249]\r\nargument
to show that, for any form of epistasis, the total response\r\nof a haploid population
is proportional to the initial total genotypic\r\nvariance. In contrast, the total
response of a diploid population is\r\nincreased by epistasis, for a given initial
genotypic variance. When\r\nselection dominates, we show that the total selection
response can\r\nonly be increased by epistasis when s\r\nome initially deleterious
alleles\r\nbecome favored as the genetic background changes. We find a sim-\r\nple
approximation for this effect and show that, in this regime, it is\r\nthe structure
of the genotype - phenotype map that matters and not\r\nthe variance components
of the population."
article_processing_charge: No
article_type: original
author:
- first_name: Tiago
full_name: Paixao, Tiago
id: 2C5658E6-F248-11E8-B48F-1D18A9856A87
last_name: Paixao
orcid: 0000-0003-2361-3953
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
citation:
ama: Paixao T, Barton NH. The effect of gene interactions on the long-term response
to selection. PNAS. 2016;113(16):4422-4427. doi:10.1073/pnas.1518830113
apa: Paixao, T., & Barton, N. H. (2016). The effect of gene interactions on
the long-term response to selection. PNAS. National Academy of Sciences.
https://doi.org/10.1073/pnas.1518830113
chicago: Paixao, Tiago, and Nicholas H Barton. “The Effect of Gene Interactions
on the Long-Term Response to Selection.” PNAS. National Academy of Sciences,
2016. https://doi.org/10.1073/pnas.1518830113.
ieee: T. Paixao and N. H. Barton, “The effect of gene interactions on the long-term
response to selection,” PNAS, vol. 113, no. 16. National Academy of Sciences,
pp. 4422–4427, 2016.
ista: Paixao T, Barton NH. 2016. The effect of gene interactions on the long-term
response to selection. PNAS. 113(16), 4422–4427.
mla: Paixao, Tiago, and Nicholas H. Barton. “The Effect of Gene Interactions on
the Long-Term Response to Selection.” PNAS, vol. 113, no. 16, National
Academy of Sciences, 2016, pp. 4422–27, doi:10.1073/pnas.1518830113.
short: T. Paixao, N.H. Barton, PNAS 113 (2016) 4422–4427.
date_created: 2018-12-11T11:51:34Z
date_published: 2016-04-19T00:00:00Z
date_updated: 2021-01-12T06:50:08Z
day: '19'
department:
- _id: NiBa
- _id: CaGu
doi: 10.1073/pnas.1518830113
ec_funded: 1
external_id:
pmid:
- '27044080'
intvolume: ' 113'
issue: '16'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4843425/
month: '04'
oa: 1
oa_version: Published Version
page: 4422 - 4427
pmid: 1
project:
- _id: 25B07788-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '250152'
name: Limits to selection in biology and in evolutionary computation
- _id: 25B1EC9E-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '618091'
name: Speed of Adaptation in Population Genetics and Evolutionary Computation
publication: PNAS
publication_status: published
publisher: National Academy of Sciences
publist_id: '5886'
quality_controlled: '1'
scopus_import: 1
status: public
title: The effect of gene interactions on the long-term response to selection
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 113
year: '2016'
...
---
_id: '1835'
abstract:
- lang: eng
text: The behaviour of gene regulatory networks (GRNs) is typically analysed using
simulation-based statistical testing-like methods. In this paper, we demonstrate
that we can replace this approach by a formal verification-like method that gives
higher assurance and scalability. We focus on Wagner’s weighted GRN model with
varying weights, which is used in evolutionary biology. In the model, weight parameters
represent the gene interaction strength that may change due to genetic mutations.
For a property of interest, we synthesise the constraints over the parameter space
that represent the set of GRNs satisfying the property. We experimentally show
that our parameter synthesis procedure computes the mutational robustness of GRNs
–an important problem of interest in evolutionary biology– more efficiently than
the classical simulation method. We specify the property in linear temporal logics.
We employ symbolic bounded model checking and SMT solving to compute the space
of GRNs that satisfy the property, which amounts to synthesizing a set of linear
constraints on the weights.
acknowledgement: "SNSF Early Postdoc.Mobility Fellowship, the grant number P2EZP2
148797.\r\n"
alternative_title:
- LNCS
author:
- first_name: Mirco
full_name: Giacobbe, Mirco
id: 3444EA5E-F248-11E8-B48F-1D18A9856A87
last_name: Giacobbe
orcid: 0000-0001-8180-0904
- first_name: Calin C
full_name: Guet, Calin C
id: 47F8433E-F248-11E8-B48F-1D18A9856A87
last_name: Guet
orcid: 0000-0001-6220-2052
- first_name: Ashutosh
full_name: Gupta, Ashutosh
id: 335E5684-F248-11E8-B48F-1D18A9856A87
last_name: Gupta
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000−0002−2985−7724
- first_name: Tiago
full_name: Paixao, Tiago
id: 2C5658E6-F248-11E8-B48F-1D18A9856A87
last_name: Paixao
orcid: 0000-0003-2361-3953
- first_name: Tatjana
full_name: Petrov, Tatjana
id: 3D5811FC-F248-11E8-B48F-1D18A9856A87
last_name: Petrov
orcid: 0000-0002-9041-0905
citation:
ama: Giacobbe M, Guet CC, Gupta A, Henzinger TA, Paixao T, Petrov T. Model checking
gene regulatory networks. 2015;9035:469-483. doi:10.1007/978-3-662-46681-0_47
apa: 'Giacobbe, M., Guet, C. C., Gupta, A., Henzinger, T. A., Paixao, T., &
Petrov, T. (2015). Model checking gene regulatory networks. Presented at the TACAS:
Tools and Algorithms for the Construction and Analysis of Systems, London, United
Kingdom: Springer. https://doi.org/10.1007/978-3-662-46681-0_47'
chicago: Giacobbe, Mirco, Calin C Guet, Ashutosh Gupta, Thomas A Henzinger, Tiago
Paixao, and Tatjana Petrov. “Model Checking Gene Regulatory Networks.” Lecture
Notes in Computer Science. Springer, 2015. https://doi.org/10.1007/978-3-662-46681-0_47.
ieee: M. Giacobbe, C. C. Guet, A. Gupta, T. A. Henzinger, T. Paixao, and T. Petrov,
“Model checking gene regulatory networks,” vol. 9035. Springer, pp. 469–483, 2015.
ista: Giacobbe M, Guet CC, Gupta A, Henzinger TA, Paixao T, Petrov T. 2015. Model
checking gene regulatory networks. 9035, 469–483.
mla: Giacobbe, Mirco, et al. Model Checking Gene Regulatory Networks. Vol.
9035, Springer, 2015, pp. 469–83, doi:10.1007/978-3-662-46681-0_47.
short: M. Giacobbe, C.C. Guet, A. Gupta, T.A. Henzinger, T. Paixao, T. Petrov, 9035
(2015) 469–483.
conference:
end_date: 2015-04-18
location: London, United Kingdom
name: 'TACAS: Tools and Algorithms for the Construction and Analysis of Systems'
start_date: 2015-04-11
date_created: 2018-12-11T11:54:16Z
date_published: 2015-04-01T00:00:00Z
date_updated: 2025-05-28T11:57:04Z
day: '01'
department:
- _id: ToHe
- _id: CaGu
- _id: NiBa
doi: 10.1007/978-3-662-46681-0_47
ec_funded: 1
intvolume: ' 9035'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1410.7704
month: '04'
oa: 1
oa_version: Preprint
page: 469 - 483
project:
- _id: 25EE3708-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '267989'
name: Quantitative Reactive Modeling
- _id: 25832EC2-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: S 11407_N23
name: Rigorous Systems Engineering
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
- _id: 25B1EC9E-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '618091'
name: Speed of Adaptation in Population Genetics and Evolutionary Computation
- _id: 25B07788-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '250152'
name: Limits to selection in biology and in evolutionary computation
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication_status: published
publisher: Springer
publist_id: '5267'
quality_controlled: '1'
related_material:
record:
- id: '1351'
relation: later_version
status: public
scopus_import: 1
series_title: Lecture Notes in Computer Science
status: public
title: Model checking gene regulatory networks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 9035
year: '2015'
...
---
_id: '1542'
abstract:
- lang: eng
text: 'The theory of population genetics and evolutionary computation have been
evolving separately for nearly 30 years. Many results have been independently
obtained in both fields and many others are unique to its respective field. We
aim to bridge this gap by developing a unifying framework for evolutionary processes
that allows both evolutionary algorithms and population genetics models to be
cast in the same formal framework. The framework we present here decomposes the
evolutionary process into its several components in order to facilitate the identification
of similarities between different models. In particular, we propose a classification
of evolutionary operators based on the defining properties of the different components.
We cast several commonly used operators from both fields into this common framework.
Using this, we map different evolutionary and genetic algorithms to different
evolutionary regimes and identify candidates with the most potential for the translation
of results between the fields. This provides a unified description of evolutionary
processes and represents a stepping stone towards new tools and results to both
fields. '
author:
- first_name: Tiago
full_name: Paixao, Tiago
id: 2C5658E6-F248-11E8-B48F-1D18A9856A87
last_name: Paixao
orcid: 0000-0003-2361-3953
- first_name: Golnaz
full_name: Badkobeh, Golnaz
last_name: Badkobeh
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
- first_name: Doğan
full_name: Çörüş, Doğan
last_name: Çörüş
- first_name: Duccuong
full_name: Dang, Duccuong
last_name: Dang
- first_name: Tobias
full_name: Friedrich, Tobias
last_name: Friedrich
- first_name: Per
full_name: Lehre, Per
last_name: Lehre
- first_name: Dirk
full_name: Sudholt, Dirk
last_name: Sudholt
- first_name: Andrew
full_name: Sutton, Andrew
last_name: Sutton
- first_name: Barbora
full_name: Trubenova, Barbora
id: 42302D54-F248-11E8-B48F-1D18A9856A87
last_name: Trubenova
orcid: 0000-0002-6873-2967
citation:
ama: Paixao T, Badkobeh G, Barton NH, et al. Toward a unifying framework for evolutionary
processes. Journal of Theoretical Biology. 2015;383:28-43. doi:10.1016/j.jtbi.2015.07.011
apa: Paixao, T., Badkobeh, G., Barton, N. H., Çörüş, D., Dang, D., Friedrich, T.,
… Trubenova, B. (2015). Toward a unifying framework for evolutionary processes.
Journal of Theoretical Biology. Elsevier. https://doi.org/10.1016/j.jtbi.2015.07.011
chicago: Paixao, Tiago, Golnaz Badkobeh, Nicholas H Barton, Doğan Çörüş, Duccuong
Dang, Tobias Friedrich, Per Lehre, Dirk Sudholt, Andrew Sutton, and Barbora Trubenova.
“Toward a Unifying Framework for Evolutionary Processes.” Journal of Theoretical
Biology. Elsevier, 2015. https://doi.org/10.1016/j.jtbi.2015.07.011.
ieee: T. Paixao et al., “Toward a unifying framework for evolutionary processes,”
Journal of Theoretical Biology, vol. 383. Elsevier, pp. 28–43, 2015.
ista: Paixao T, Badkobeh G, Barton NH, Çörüş D, Dang D, Friedrich T, Lehre P, Sudholt
D, Sutton A, Trubenova B. 2015. Toward a unifying framework for evolutionary processes. Journal
of Theoretical Biology. 383, 28–43.
mla: Paixao, Tiago, et al. “Toward a Unifying Framework for Evolutionary Processes.”
Journal of Theoretical Biology, vol. 383, Elsevier, 2015, pp. 28–43, doi:10.1016/j.jtbi.2015.07.011.
short: T. Paixao, G. Badkobeh, N.H. Barton, D. Çörüş, D. Dang, T. Friedrich, P.
Lehre, D. Sudholt, A. Sutton, B. Trubenova, Journal of Theoretical Biology 383
(2015) 28–43.
date_created: 2018-12-11T11:52:37Z
date_published: 2015-10-21T00:00:00Z
date_updated: 2021-01-12T06:51:29Z
day: '21'
ddc:
- '570'
department:
- _id: NiBa
- _id: CaGu
doi: 10.1016/j.jtbi.2015.07.011
ec_funded: 1
file:
- access_level: open_access
checksum: 33b60ecfea60764756a9ee9df5eb65ca
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:16:53Z
date_updated: 2020-07-14T12:45:01Z
file_id: '5244'
file_name: IST-2016-483-v1+1_1-s2.0-S0022519315003409-main.pdf
file_size: 595307
relation: main_file
file_date_updated: 2020-07-14T12:45:01Z
has_accepted_license: '1'
intvolume: ' 383'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
page: 28 - 43
project:
- _id: 25B1EC9E-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '618091'
name: Speed of Adaptation in Population Genetics and Evolutionary Computation
- _id: 25B07788-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '250152'
name: Limits to selection in biology and in evolutionary computation
publication: ' Journal of Theoretical Biology'
publication_status: published
publisher: Elsevier
publist_id: '5629'
pubrep_id: '483'
quality_controlled: '1'
scopus_import: 1
status: public
title: Toward a unifying framework for evolutionary processes
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 383
year: '2015'
...
---
_id: '1430'
abstract:
- lang: eng
text: Evolutionary algorithms (EAs) form a popular optimisation paradigm inspired
by natural evolution. In recent years the field of evolutionary computation has
developed a rigorous analytical theory to analyse their runtime on many illustrative
problems. Here we apply this theory to a simple model of natural evolution. In
the Strong Selection Weak Mutation (SSWM) evolutionary regime the time between
occurrence of new mutations is much longer than the time it takes for a new beneficial
mutation to take over the population. In this situation, the population only contains
copies of one genotype and evolution can be modelled as a (1+1)-type process where
the probability of accepting a new genotype (improvements or worsenings) depends
on the change in fitness. We present an initial runtime analysis of SSWM, quantifying
its performance for various parameters and investigating differences to the (1+1)
EA. We show that SSWM can have a moderate advantage over the (1+1) EA at crossing
fitness valleys and study an example where SSWM outperforms the (1+1) EA by taking
advantage of information on the fitness gradient.
author:
- first_name: Tiago
full_name: Paixao, Tiago
id: 2C5658E6-F248-11E8-B48F-1D18A9856A87
last_name: Paixao
orcid: 0000-0003-2361-3953
- first_name: Dirk
full_name: Sudholt, Dirk
last_name: Sudholt
- first_name: Jorge
full_name: Heredia, Jorge
last_name: Heredia
- first_name: Barbora
full_name: Trubenova, Barbora
id: 42302D54-F248-11E8-B48F-1D18A9856A87
last_name: Trubenova
orcid: 0000-0002-6873-2967
citation:
ama: 'Paixao T, Sudholt D, Heredia J, Trubenova B. First steps towards a runtime
comparison of natural and artificial evolution. In: Proceedings of the 2015
Annual Conference on Genetic and Evolutionary Computation. ACM; 2015:1455-1462.
doi:10.1145/2739480.2754758'
apa: 'Paixao, T., Sudholt, D., Heredia, J., & Trubenova, B. (2015). First steps
towards a runtime comparison of natural and artificial evolution. In Proceedings
of the 2015 Annual Conference on Genetic and Evolutionary Computation (pp.
1455–1462). Madrid, Spain: ACM. https://doi.org/10.1145/2739480.2754758'
chicago: Paixao, Tiago, Dirk Sudholt, Jorge Heredia, and Barbora Trubenova. “First
Steps towards a Runtime Comparison of Natural and Artificial Evolution.” In Proceedings
of the 2015 Annual Conference on Genetic and Evolutionary Computation, 1455–62.
ACM, 2015. https://doi.org/10.1145/2739480.2754758.
ieee: T. Paixao, D. Sudholt, J. Heredia, and B. Trubenova, “First steps towards
a runtime comparison of natural and artificial evolution,” in Proceedings of
the 2015 Annual Conference on Genetic and Evolutionary Computation, Madrid,
Spain, 2015, pp. 1455–1462.
ista: 'Paixao T, Sudholt D, Heredia J, Trubenova B. 2015. First steps towards a
runtime comparison of natural and artificial evolution. Proceedings of the 2015
Annual Conference on Genetic and Evolutionary Computation. GECCO: Genetic and
evolutionary computation conference, 1455–1462.'
mla: Paixao, Tiago, et al. “First Steps towards a Runtime Comparison of Natural
and Artificial Evolution.” Proceedings of the 2015 Annual Conference on Genetic
and Evolutionary Computation, ACM, 2015, pp. 1455–62, doi:10.1145/2739480.2754758.
short: T. Paixao, D. Sudholt, J. Heredia, B. Trubenova, in:, Proceedings of the
2015 Annual Conference on Genetic and Evolutionary Computation, ACM, 2015, pp.
1455–1462.
conference:
end_date: 2015-07-15
location: Madrid, Spain
name: 'GECCO: Genetic and evolutionary computation conference'
start_date: 2015-07-11
date_created: 2018-12-11T11:51:58Z
date_published: 2015-07-11T00:00:00Z
date_updated: 2021-01-12T06:50:41Z
day: '11'
department:
- _id: NiBa
- _id: CaGu
doi: 10.1145/2739480.2754758
ec_funded: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1504.06260
month: '07'
oa: 1
oa_version: Preprint
page: 1455 - 1462
project:
- _id: 25B1EC9E-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '618091'
name: Speed of Adaptation in Population Genetics and Evolutionary Computation
publication: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary
Computation
publication_status: published
publisher: ACM
publist_id: '5768'
quality_controlled: '1'
scopus_import: 1
status: public
title: First steps towards a runtime comparison of natural and artificial evolution
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2015'
...