Coevolutionary algorithms approach problems for which no function for evaluating potential solutions is present or known. Instead, algorithms rely on the aggregation of outcomes from interactions among evolving entities in order to make selection decisions. Given the lack of an explicit yardstick, understanding the dynamics of coevolutionary algorithms, judging whether a given algorithm is progressing, and designing effective new algorithms present unique challenges unlike those faced by optimization or evolutionary algorithms. The purpose of this chapter is to provide a foundational understanding of coevolutionary algorithms and to highlight critical theoretical and empirical work done over the last two decades. This chapter outlines the ends and means of coevolutionary algorithms: what they are meant to find, and how they should find it.
Handbook of Natural Computing
The inspiration for coevolutionary algorithms (CoEAs) is the same as for traditional evolutionay algorithms (EAs): attempt to harness the Darwinian notions of heredity and survival of the fittest for simulation or problem-solving purposes. To put it simply, a representation is chosen to encode some aspects of potential solutions to a problem into individuals, those individuals are altered during search using genetic-like variation operators such as mutation and crossover, and search is directed by selecting better individuals as determined by a fitness evaluation. With any luck, the iteration of these steps will eventually lead to high-quality solutions to a problem, if problem solving is the aim, or to interesting or realistic system behavior.
Variation on a theme. Search. Problem solving. Solutions. Design. Directed Search.
Why are the authors of this chapter mistaken?