Selection methods regulate evolution of cooperation in digital evolution

被引:0
作者
Lichocki, Pawel [1 ]
Floreano, Dario [1 ]
Keller, Laurent [2 ]
机构
[1] Ecole Polytech Fed Lausanne, Lab Intelligent Syst, CH-1015 Lausanne, Switzerland
[2] Univ Lausanne, Dept Ecol & Evolut, CH-1015 Lausanne, Switzerland
基金
瑞士国家科学基金会;
关键词
selection methods; digital evolution; cooperation; Prisoner's Dilemma; DIVISION-OF-LABOR; NATURAL-SELECTION; INCLUSIVE FITNESS; PRISONERS-DILEMMA; SOCIAL EVOLUTION; ROBOTS; COMMUNICATION; ENVIRONMENTS; POPULATIONS; EMERGENCE;
D O I
10.1098/rsif.2013.0743
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
A key, yet often neglected, component of digital evolution and evolutionary models is the 'selection method' which assigns fitness (number of offspring) to individuals based on their performance scores (efficiency in performing tasks). Here, we study with formal analysis and numerical experiments the evolution of cooperation under the five most common selection methods (proportionate, rank, truncation-proportionate, truncation-uniform and tournament). We consider related individuals engaging in a Prisoner's Dilemma game where individuals can either cooperate or defect. A cooperator pays a cost, whereas its partner receives a benefit, which affect their performance scores. These performance scores are translated into fitness by one of the five selection methods. We show that cooperation is positively associated with the relatedness between individuals under all selection methods. By contrast, the change in the performance benefit of cooperation affects the populations' average level of cooperation only under the proportionate methods. We also demonstrate that the truncation and tournament methods may introduce negative frequency-dependence and lead to the evolution of polymorphic populations. Using the example of the evolution of cooperation, we show that the choice of selection method, though it is often marginalized, can considerably affect the evolutionary dynamics.
引用
收藏
页数:8
相关论文
共 84 条
  • [1] Digital genetics: unravelling the genetic basis of evolution
    Adami, C
    [J]. NATURE REVIEWS GENETICS, 2006, 7 (02) : 109 - 118
  • [2] Phylogenetic and ontogenetic learning in a colony of interacting robots
    Agah, A
    Bekey, GA
    [J]. AUTONOMOUS ROBOTS, 1997, 4 (01) : 85 - 100
  • [3] Online interactive neuro-evolution
    Agogino, A
    Stanley, K
    Miikkulainen, R
    [J]. NEURAL PROCESSING LETTERS, 2000, 11 (01) : 29 - 37
  • [4] [Anonymous], 1971, The Insect Societies
  • [5] [Anonymous], 1991, Foundations of Genetic Algorithms
  • [6] Axelrod R., 1987, GENETIC ALGORITHMS S, V24, P32
  • [7] Back T., 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence (Cat. No.94TH0650-2), P57, DOI 10.1109/ICEC.1994.350042
  • [8] Evolving mobile robots able to display collective behaviors
    Baldassarre, G
    Nolfi, S
    Parisi, D
    [J]. ARTIFICIAL LIFE, 2003, 9 (03) : 255 - 267
  • [9] Blickle T., 1995, MATH ANAL TOURNAMENT, V95, P9
  • [10] The evolution of strong reciprocity: cooperation in heterogeneous populations
    Bowles, S
    Gintis, H
    [J]. THEORETICAL POPULATION BIOLOGY, 2004, 65 (01) : 17 - 28