--- _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 license: https://creativecommons.org/licenses/by-nc/4.0/ 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 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: '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: '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 checksum: 7873f665a0c598ac747c908f34cb14b9 content_type: application/pdf creator: system date_created: 2018-12-12T10:10:19Z date_updated: 2020-07-14T12:44:44Z file_id: '4805' file_name: IST-2016-658-v1+1_s00453-016-0212-1.pdf file_size: 710206 relation: main_file file_date_updated: 2020-07-14T12:44:44Z has_accepted_license: '1' intvolume: ' 78' isi: 1 issue: '2' language: - iso: eng month: '06' oa: 1 oa_version: Published Version page: 681 - 713 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_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: '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: '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: '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' ... --- _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: '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: - access_level: open_access checksum: d3a4a58ef4bd3b3e2f32b7fd7af4a743 content_type: application/pdf creator: system date_created: 2018-12-12T10:09:07Z date_updated: 2020-07-14T12:45:17Z file_id: '4730' file_name: IST-2016-453-v1+1_journal.pone.0126907.pdf file_size: 2748982 relation: main_file file_date_updated: 2020-07-14T12:45:17Z has_accepted_license: '1' intvolume: ' 10' issue: '5' language: - iso: eng 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' quality_controlled: '1' related_material: record: - id: '9715' relation: research_data status: public - id: '9772' 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: '9772' article_processing_charge: No 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. Description of the agent based simulations. 2015. doi:10.1371/journal.pone.0126907.s003 apa: Trubenova, B., Novak, S., & Hager, R. (2015). Description of the agent based simulations. Public Library of Science. https://doi.org/10.1371/journal.pone.0126907.s003 chicago: Trubenova, Barbora, Sebastian Novak, and Reinmar Hager. “Description of the Agent Based Simulations.” Public Library of Science, 2015. https://doi.org/10.1371/journal.pone.0126907.s003. ieee: B. Trubenova, S. Novak, and R. Hager, “Description of the agent based simulations.” Public Library of Science, 2015. ista: Trubenova B, Novak S, Hager R. 2015. Description of the agent based simulations, Public Library of Science, 10.1371/journal.pone.0126907.s003. mla: Trubenova, Barbora, et al. Description of the Agent Based Simulations. Public Library of Science, 2015, doi:10.1371/journal.pone.0126907.s003. short: B. Trubenova, S. Novak, R. Hager, (2015). date_created: 2021-08-05T12:55:20Z date_published: 2015-05-18T00:00:00Z date_updated: 2023-02-23T10:15:25Z day: '18' department: - _id: NiBa doi: 10.1371/journal.pone.0126907.s003 month: '05' oa_version: Published Version publisher: Public Library of Science related_material: record: - id: '1809' relation: used_in_publication status: public status: public title: Description of the agent based simulations type: research_data_reference user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf year: '2015' ... --- _id: '9715' article_processing_charge: No 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. Mathematical inference of the results. 2015. doi:10.1371/journal.pone.0126907.s001 apa: Trubenova, B., Novak, S., & Hager, R. (2015). Mathematical inference of the results. Public Library of Science. https://doi.org/10.1371/journal.pone.0126907.s001 chicago: Trubenova, Barbora, Sebastian Novak, and Reinmar Hager. “Mathematical Inference of the Results.” Public Library of Science, 2015. https://doi.org/10.1371/journal.pone.0126907.s001. ieee: B. Trubenova, S. Novak, and R. Hager, “Mathematical inference of the results.” Public Library of Science, 2015. ista: Trubenova B, Novak S, Hager R. 2015. Mathematical inference of the results, Public Library of Science, 10.1371/journal.pone.0126907.s001. mla: Trubenova, Barbora, et al. Mathematical Inference of the Results. Public Library of Science, 2015, doi:10.1371/journal.pone.0126907.s001. short: B. Trubenova, S. Novak, R. Hager, (2015). date_created: 2021-07-23T12:11:30Z date_published: 2015-05-18T00:00:00Z date_updated: 2023-02-23T10:15:25Z day: '18' department: - _id: NiBa doi: 10.1371/journal.pone.0126907.s001 month: '05' oa_version: Published Version publisher: Public Library of Science related_material: record: - id: '1809' relation: used_in_publication status: public status: public title: Mathematical inference of the results type: research_data_reference user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf year: '2015' ...