Multilevel Evolutionary Algorithm that Optimizes the Structure of Scale-Free Networks for the Promotion of Cooperation in the Prisoner’s Dilemma game

Abstract Understanding the emergence of cooperation has long been a challenge across disciplines. Even if network reciprocity reflected the importance of population structure in promoting cooperation, it remains an open question how population structures can be optimized, thereby enhancing cooperati...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Penghui Liu, Jing Liu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2b13c72332e54959be7d7e9f683f05da
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:2b13c72332e54959be7d7e9f683f05da
record_format	dspace
spelling	oai:doaj.org-article:2b13c72332e54959be7d7e9f683f05da2021-12-02T15:05:57ZMultilevel Evolutionary Algorithm that Optimizes the Structure of Scale-Free Networks for the Promotion of Cooperation in the Prisoner’s Dilemma game10.1038/s41598-017-04010-22045-2322https://doaj.org/article/2b13c72332e54959be7d7e9f683f05da2017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-04010-2https://doaj.org/toc/2045-2322Abstract Understanding the emergence of cooperation has long been a challenge across disciplines. Even if network reciprocity reflected the importance of population structure in promoting cooperation, it remains an open question how population structures can be optimized, thereby enhancing cooperation. In this paper, we attempt to apply the evolutionary algorithm (EA) to solve this highly complex problem. However, as it is hard to evaluate the fitness (cooperation level) of population structures, simply employing the canonical evolutionary algorithm (EA) may fail in optimization. Thus, we propose a new EA variant named mlEA-CPD-SFN to promote the cooperation level of scale-free networks (SFNs) in the Prisoner’s Dilemma Game (PDG). Meanwhile, to verify the preceding conclusions may not be applied to this problem, we also provide the optimization results of the comparative experiment (EAcluster), which optimizes the clustering coefficient of structures. Even if preceding research concluded that highly clustered scale-free networks enhance cooperation, we find EAcluster does not perform desirably, while mlEA-CPD-SFN performs efficiently in different optimization environments. We hope that mlEA-CPD-SFN may help promote the structure of species in nature and that more general properties that enhance cooperation can be learned from the output structures.Penghui LiuJing LiuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Penghui Liu Jing Liu Multilevel Evolutionary Algorithm that Optimizes the Structure of Scale-Free Networks for the Promotion of Cooperation in the Prisoner’s Dilemma game
description	Abstract Understanding the emergence of cooperation has long been a challenge across disciplines. Even if network reciprocity reflected the importance of population structure in promoting cooperation, it remains an open question how population structures can be optimized, thereby enhancing cooperation. In this paper, we attempt to apply the evolutionary algorithm (EA) to solve this highly complex problem. However, as it is hard to evaluate the fitness (cooperation level) of population structures, simply employing the canonical evolutionary algorithm (EA) may fail in optimization. Thus, we propose a new EA variant named mlEA-CPD-SFN to promote the cooperation level of scale-free networks (SFNs) in the Prisoner’s Dilemma Game (PDG). Meanwhile, to verify the preceding conclusions may not be applied to this problem, we also provide the optimization results of the comparative experiment (EAcluster), which optimizes the clustering coefficient of structures. Even if preceding research concluded that highly clustered scale-free networks enhance cooperation, we find EAcluster does not perform desirably, while mlEA-CPD-SFN performs efficiently in different optimization environments. We hope that mlEA-CPD-SFN may help promote the structure of species in nature and that more general properties that enhance cooperation can be learned from the output structures.
format	article
author	Penghui Liu Jing Liu
author_facet	Penghui Liu Jing Liu
author_sort	Penghui Liu
title	Multilevel Evolutionary Algorithm that Optimizes the Structure of Scale-Free Networks for the Promotion of Cooperation in the Prisoner’s Dilemma game
title_short	Multilevel Evolutionary Algorithm that Optimizes the Structure of Scale-Free Networks for the Promotion of Cooperation in the Prisoner’s Dilemma game
title_full	Multilevel Evolutionary Algorithm that Optimizes the Structure of Scale-Free Networks for the Promotion of Cooperation in the Prisoner’s Dilemma game
title_fullStr	Multilevel Evolutionary Algorithm that Optimizes the Structure of Scale-Free Networks for the Promotion of Cooperation in the Prisoner’s Dilemma game
title_full_unstemmed	Multilevel Evolutionary Algorithm that Optimizes the Structure of Scale-Free Networks for the Promotion of Cooperation in the Prisoner’s Dilemma game
title_sort	multilevel evolutionary algorithm that optimizes the structure of scale-free networks for the promotion of cooperation in the prisoner’s dilemma game
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/2b13c72332e54959be7d7e9f683f05da
work_keys_str_mv	AT penghuiliu multilevelevolutionaryalgorithmthatoptimizesthestructureofscalefreenetworksforthepromotionofcooperationintheprisonersdilemmagame AT jingliu multilevelevolutionaryalgorithmthatoptimizesthestructureofscalefreenetworksforthepromotionofcooperationintheprisonersdilemmagame
_version_	1718388633562513408

Multilevel Evolutionary Algorithm that Optimizes the Structure of Scale-Free Networks for the Promotion of Cooperation in the Prisoner’s Dilemma game

Ejemplares similares