This paper introduces a new collective learning genetic algorithm (CLGA) which employs individual learning to do intelligent recombination based on a cooperative exchange of knowledge between interacting chromosomes. Each individual in the population observes a unique set of features in the chromosomes with which it interacts in order to explicitly estimate the average fitnesses of schemata in the population, and to use that information to guide recombination. The stages of evolution are still controlled by a global algorithm, but much of the control in the CLGA is distributed among chromosomes that are individually responsible for recombination, mutation and selection. The effectiveness of the approach is demonstrated on random problems generated by an NK-Landscape problem generator. Preliminary results suggest that the CLGA may be especially effective for searching for solutions to highly epistatic, non-separable problems, a class of problems traditionally difficult for regular GAs.
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