Memetic Crossover for Genetic Programming: Evolution Through Imitation

For problems where the evaluation of an individual is the dominant factor in the total computation time of the evolutionary process, minimizing the number of evaluations becomes critical. This paper introduces a new crossover operator for genetic programming, memetic crossover, that reduces the number of evaluations required to find an ideal solution. Memetic crossover selects individuals and crossover points by evaluating the observed strengths and weaknesses within areas of the problem. An individual that has done poorly in some parts of the problem may then imitate an individual that did well on those same parts. This results in an intelligent search of the feature-space and, therefore, fewer evaluations.