Case studies in applying fitness distributions in evolutionary algorithms. II. Comparing the improvements from crossover and Gaussian mutation on simple neural networks
A. Jain, David B. Fogel · 2002
Previous efforts in applying fitness distributions of Gaussian mutation for optimizing simple neural networks in the XOR problem are extended by conducting a similar analysis for three types of crossover operators. One-point, two-point and uniform crossover are applied to the best-evolved neural networks at each generation in an evolutionary trial. The maximum expected improvement under Gaussian mutation with a single fixed standard deviation is then compared to that which can be obtained using crossover. The results indicate that the benefits of each type of crossover varies as a function of the generation number. Furthermore, these fitness profiles are notably similar (i.e., there is little functional difference between the various crossover operators). This does not support a building block hypothesis for explaining the gains that can be made via recombination. The results indicate cases where mutation alone can outperform recombination and vice versa.