Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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This paper explores the feasibility of computer simulation of evolving populations of social animals in nature, both from the anatomical and socio-environmental viewpoints, addressing the gap between the algorithms for evolution of digital objects, and the evolution of species in nature. The main components of ant body are mathematically described within the function representation framework; the parameters directly determine both the visual characteristics of the ant as well as the body characteristics encoded by the genome. The environmental diversification of ant subspecies is studied for fungus-growing ants, in which single-queen mating reproduction couples with large size of accessory male glands, while multiple-queen mating correlates to large size of accessory testes. Our results show that within an environment of restricted resources, both competing modes of sexual reproduction survive. The frequency with which either mode becomes dominant in the population is driven by the value of the mutation probability. The function representation model should be useful also in the simulation of other simple animal species, because of the ease in relating the genome parameters to computer visualization tools.