A General Model of Co-evolution for Genetic Algorithms

Abstract

. Compared with natural systems, Genetic Algorithms have a limited adaptive capacity, i.e. they get quite frequently trapped at local optima and they are poor at tracking moving optima in dynamic environments. This paper describes a general, formal model of co-evolution, the Linear Model of Symbiosis, that allows for the concise, unified expression of all types of coevolutionary relations studied in ecology. Experiments on several difficult problems support our assumption that the addition of the Linear Model of Symbiosis to a canonical Genetic Algorithm can remedy the above shortcomings. 1 Introduction Although Genetic Algorithms (GAs) have demonstrated their robustness and efficiency as search and learning techniques in many application domains, they often suffer from the problem of premature convergence: as the individuals in an evolving population approach a local optimum, the resulting loss of genetic diversity may prevent the GA from finding the global optimum1. A related problem arises in dynamic environments even if the landscape is unimodal. The loss of diversity in the population resulting from successfully converging to an optimum renders the GA incapable of tracking the optimum if it should drift to another point in gene space. Currently there are a limited number of mechanisms available, short of a restart, that allow the GA to find a new optimum (see [2, 4]).