An Analysis of Memetic Crossover's Impact on a Population

In problem domains such as robotic control, where the evaluation of an individual significantly dominates the rest of the evolutionary process with respect to time, the viability of an evolutionary approach can be called into question. In an effort to minimize the number of evaluations by maximizing the learning that takes place during an evaluation, a new crossover operator for genetic programming, memetic crossover, was recently introduced. This work analyzes the genealogical impact of this operator at varying levels. Although diversity, both in terms of individuals and nodes, is reduced in memetic crossover, we show that memetic crossover is capable of working with standard crossover to consistently improve the fitness of individuals.