---
_id: '11640'
abstract:
- lang: eng
text: Spatially explicit population genetic models have long been developed, yet
have rarely been used to test hypotheses about the spatial distribution of genetic
diversity or the genetic divergence between populations. Here, we use spatially
explicit coalescence simulations to explore the properties of the island and the
two-dimensional stepping stone models under a wide range of scenarios with spatio-temporal
variation in deme size. We avoid the simulation of genetic data, using the fact
that under the studied models, summary statistics of genetic diversity and divergence
can be approximated from coalescence times. We perform the simulations using gridCoal,
a flexible spatial wrapper for the software msprime (Kelleher et al., 2016, Theoretical
Population Biology, 95, 13) developed herein. In gridCoal, deme sizes can change
arbitrarily across space and time, as well as migration rates between individual
demes. We identify different factors that can cause a deviation from theoretical
expectations, such as the simulation time in comparison to the effective deme
size and the spatio-temporal autocorrelation across the grid. Our results highlight
that FST, a measure of the strength of population structure, principally depends
on recent demography, which makes it robust to temporal variation in deme size.
In contrast, the amount of genetic diversity is dependent on the distant past
when Ne is large, therefore longer run times are needed to estimate Ne than FST.
Finally, we illustrate the use of gridCoal on a real-world example, the range
expansion of silver fir (Abies alba Mill.) since the last glacial maximum, using
different degrees of spatio-temporal variation in deme size.
acknowledgement: ES was supported by an IST studentship provided by IST Austria. BT
was funded by the European Union's Horizon 2020 research and innovation programme
under the Marie Sklodowska-Curie Independent Fellowship (704172, RACE). This project
received further funding awarded to KC from the Swiss National Science Foundation
(SNSF CRSK-3_190288) and the Swiss Federal Research Institute WSL. We thank Nick
Barton for many invaluable discussions and his comments on the thesis chapter and
this manuscript. We thank Peter Ralph and Jerome Kelleher for useful discussions
and Bisschop Gertjan for comments on this manuscript. We thank Fortunat Joos for
providing us with the raw data from the LPX-Bern model for silver fir, and Willy
Tinner for helpful insights about the demographic history of silver fir. We also
thank the editor Alana Alexander for useful comments and advice on the manuscript.
Open access funding provided by Eidgenossische Technische Hochschule Zurich.
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Eniko
full_name: Szep, Eniko
id: 485BB5A4-F248-11E8-B48F-1D18A9856A87
last_name: Szep
- first_name: Barbora
full_name: Trubenova, Barbora
id: 42302D54-F248-11E8-B48F-1D18A9856A87
last_name: Trubenova
orcid: 0000-0002-6873-2967
- first_name: Katalin
full_name: Csilléry, Katalin
last_name: Csilléry
citation:
ama: Szep E, Trubenova B, Csilléry K. Using gridCoal to assess whether standard
population genetic theory holds in the presence of spatio-temporal heterogeneity
in population size. Molecular Ecology Resources. 2022;22(8):2941-2955.
doi:10.1111/1755-0998.13676
apa: Szep, E., Trubenova, B., & Csilléry, K. (2022). Using gridCoal to assess
whether standard population genetic theory holds in the presence of spatio-temporal
heterogeneity in population size. Molecular Ecology Resources. Wiley. https://doi.org/10.1111/1755-0998.13676
chicago: Szep, Eniko, Barbora Trubenova, and Katalin Csilléry. “Using GridCoal to
Assess Whether Standard Population Genetic Theory Holds in the Presence of Spatio-Temporal
Heterogeneity in Population Size.” Molecular Ecology Resources. Wiley,
2022. https://doi.org/10.1111/1755-0998.13676.
ieee: E. Szep, B. Trubenova, and K. Csilléry, “Using gridCoal to assess whether
standard population genetic theory holds in the presence of spatio-temporal heterogeneity
in population size,” Molecular Ecology Resources, vol. 22, no. 8. Wiley,
pp. 2941–2955, 2022.
ista: Szep E, Trubenova B, Csilléry K. 2022. Using gridCoal to assess whether standard
population genetic theory holds in the presence of spatio-temporal heterogeneity
in population size. Molecular Ecology Resources. 22(8), 2941–2955.
mla: Szep, Eniko, et al. “Using GridCoal to Assess Whether Standard Population Genetic
Theory Holds in the Presence of Spatio-Temporal Heterogeneity in Population Size.”
Molecular Ecology Resources, vol. 22, no. 8, Wiley, 2022, pp. 2941–55,
doi:10.1111/1755-0998.13676.
short: E. Szep, B. Trubenova, K. Csilléry, Molecular Ecology Resources 22 (2022)
2941–2955.
date_created: 2022-07-24T22:01:43Z
date_published: 2022-11-01T00:00:00Z
date_updated: 2023-08-03T12:11:01Z
day: '01'
ddc:
- '570'
department:
- _id: NiBa
doi: 10.1111/1755-0998.13676
ec_funded: 1
external_id:
isi:
- '000825873600001'
file:
- access_level: open_access
checksum: 3102e203e77b884bffffdbe8e548da88
content_type: application/pdf
creator: dernst
date_created: 2023-02-02T08:11:23Z
date_updated: 2023-02-02T08:11:23Z
file_id: '12477'
file_name: 2022_MolecularEcologyRes_Szep.pdf
file_size: 6431779
relation: main_file
success: 1
file_date_updated: 2023-02-02T08:11:23Z
has_accepted_license: '1'
intvolume: ' 22'
isi: 1
issue: '8'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
page: 2941-2955
project:
- _id: 25AEDD42-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '704172'
name: Rate of Adaptation in Changing Environment
publication: Molecular Ecology Resources
publication_identifier:
eissn:
- 1755-0998
issn:
- 1755-098X
publication_status: published
publisher: Wiley
quality_controlled: '1'
scopus_import: '1'
status: public
title: Using gridCoal to assess whether standard population genetic theory holds in
the presence of spatio-temporal heterogeneity in population size
tmp:
image: /images/cc_by_nc.png
legal_code_url: https://creativecommons.org/licenses/by-nc/4.0/legalcode
name: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
short: CC BY-NC (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 22
year: '2022'
...
---
_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
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: '6795'
abstract:
- lang: eng
text: The green‐beard effect is one proposed mechanism predicted to underpin the
evolu‐tion of altruistic behavior. It relies on the recognition and the selective
help of altruists to each other in order to promote and sustain altruistic behavior.
However, this mechanism has often been dismissed as unlikely or uncommon, as it
is assumed that both the signaling trait and altruistic trait need to be encoded
by the same gene or through tightly linked genes. Here, we use models of indirect
genetic effects (IGEs) to find the minimum correlation between the signaling and
altruistic trait required for the evolution of the latter. We show that this correlation
threshold depends on the strength of the interaction (influence of the green beard
on the expression of the altruistic trait), as well as the costs and benefits
of the altruistic behavior. We further show that this correlation does not necessarily
have to be high and support our analytical results by simulations.
article_processing_charge: No
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: Reinmar
full_name: Hager, Reinmar
last_name: Hager
citation:
ama: Trubenova B, Hager R. Green beards in the light of indirect genetic effects.
Ecology and Evolution. 2019;9(17):9597-9608. doi:10.1002/ece3.5484
apa: Trubenova, B., & Hager, R. (2019). Green beards in the light of indirect
genetic effects. Ecology and Evolution. Wiley. https://doi.org/10.1002/ece3.5484
chicago: Trubenova, Barbora, and Reinmar Hager. “Green Beards in the Light of Indirect
Genetic Effects.” Ecology and Evolution. Wiley, 2019. https://doi.org/10.1002/ece3.5484.
ieee: B. Trubenova and R. Hager, “Green beards in the light of indirect genetic
effects,” Ecology and Evolution, vol. 9, no. 17. Wiley, pp. 9597–9608,
2019.
ista: Trubenova B, Hager R. 2019. Green beards in the light of indirect genetic
effects. Ecology and Evolution. 9(17), 9597–9608.
mla: Trubenova, Barbora, and Reinmar Hager. “Green Beards in the Light of Indirect
Genetic Effects.” Ecology and Evolution, vol. 9, no. 17, Wiley, 2019, pp.
9597–608, doi:10.1002/ece3.5484.
short: B. Trubenova, R. Hager, Ecology and Evolution 9 (2019) 9597–9608.
date_created: 2019-08-11T21:59:24Z
date_published: 2019-09-01T00:00:00Z
date_updated: 2023-08-29T07:03:10Z
day: '01'
ddc:
- '576'
department:
- _id: NiBa
doi: 10.1002/ece3.5484
ec_funded: 1
external_id:
isi:
- '000479973400001'
file:
- access_level: open_access
checksum: adcb70af4901977d95b8747eeee01bd7
content_type: application/pdf
creator: dernst
date_created: 2019-08-12T07:30:30Z
date_updated: 2020-07-14T12:47:40Z
file_id: '6799'
file_name: 2019_EcologyEvolution_Trubenova.pdf
file_size: 2839636
relation: main_file
file_date_updated: 2020-07-14T12:47:40Z
has_accepted_license: '1'
intvolume: ' 9'
isi: 1
issue: '17'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: 9597-9608
project:
- _id: 25AEDD42-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '704172'
name: Rate of Adaptation in Changing Environment
publication: Ecology and Evolution
publication_identifier:
eissn:
- '20457758'
publication_status: published
publisher: Wiley
quality_controlled: '1'
scopus_import: '1'
status: public
title: Green beards in the light of indirect genetic effects
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: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 9
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
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: '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:
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checksum: 7873f665a0c598ac747c908f34cb14b9
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:10:19Z
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file_size: 710206
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language:
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oa: 1
oa_version: Published Version
page: 681 - 713
project:
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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:
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: 78
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:
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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: '1809'
abstract:
- lang: eng
text: 'Background: Indirect genetic effects (IGEs) occur when genes expressed in
one individual alter the expression of traits in social partners. Previous studies
focused on the evolutionary consequences and evolutionary dynamics of IGEs, using
equilibrium solutions to predict phenotypes in subsequent generations. However,
whether or not such steady states may be reached may depend on the dynamics of
interactions themselves. Results: In our study, we focus on the dynamics of social
interactions and indirect genetic effects and investigate how they modify phenotypes
over time. Unlike previous IGE studies, we do not analyse evolutionary dynamics;
rather we consider within-individual phenotypic changes, also referred to as phenotypic
plasticity. We analyse iterative interactions, when individuals interact in a
series of discontinuous events, and investigate the stability of steady state
solutions and the dependence on model parameters, such as population size, strength,
and the nature of interactions. We show that for interactions where a feedback
loop occurs, the possible parameter space of interaction strength is fairly limited,
affecting the evolutionary consequences of IGEs. We discuss the implications of
our results for current IGE model predictions and their limitations.'
author:
- first_name: Barbora
full_name: Trubenova, Barbora
id: 42302D54-F248-11E8-B48F-1D18A9856A87
last_name: Trubenova
orcid: 0000-0002-6873-2967
- first_name: Sebastian
full_name: Novak, Sebastian
id: 461468AE-F248-11E8-B48F-1D18A9856A87
last_name: Novak
- first_name: Reinmar
full_name: Hager, Reinmar
last_name: Hager
citation:
ama: Trubenova B, Novak S, Hager R. Indirect genetic effects and the dynamics of
social interactions. PLoS One. 2015;10(5). doi:10.1371/journal.pone.0126907
apa: Trubenova, B., Novak, S., & Hager, R. (2015). Indirect genetic effects
and the dynamics of social interactions. PLoS One. Public Library of Science.
https://doi.org/10.1371/journal.pone.0126907
chicago: Trubenova, Barbora, Sebastian Novak, and Reinmar Hager. “Indirect Genetic
Effects and the Dynamics of Social Interactions.” PLoS One. Public Library
of Science, 2015. https://doi.org/10.1371/journal.pone.0126907.
ieee: B. Trubenova, S. Novak, and R. Hager, “Indirect genetic effects and the dynamics
of social interactions,” PLoS One, vol. 10, no. 5. Public Library of Science,
2015.
ista: Trubenova B, Novak S, Hager R. 2015. Indirect genetic effects and the dynamics
of social interactions. PLoS One. 10(5).
mla: Trubenova, Barbora, et al. “Indirect Genetic Effects and the Dynamics of Social
Interactions.” PLoS One, vol. 10, no. 5, Public Library of Science, 2015,
doi:10.1371/journal.pone.0126907.
short: B. Trubenova, S. Novak, R. Hager, PLoS One 10 (2015).
date_created: 2018-12-11T11:54:07Z
date_published: 2015-05-18T00:00:00Z
date_updated: 2023-02-23T14:07:48Z
day: '18'
ddc:
- '570'
- '576'
department:
- _id: NiBa
doi: 10.1371/journal.pone.0126907
file:
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month: '05'
oa: 1
oa_version: Published Version
publication: PLoS One
publication_status: published
publisher: Public Library of Science
publist_id: '5299'
pubrep_id: '453'
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related_material:
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relation: research_data
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relation: research_data
status: public
scopus_import: 1
status: public
title: Indirect genetic effects and the dynamics of social interactions
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: 10
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
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ieee: B. Trubenova, S. Novak, and R. Hager, “Mathematical inference of the results.”
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short: B. Trubenova, S. Novak, R. Hager, (2015).
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chicago: Trubenova, Barbora, Sebastian Novak, and Reinmar Hager. “Description of
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ieee: B. Trubenova, S. Novak, and R. Hager, “Description of the agent based simulations.”
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