Exploratory operators allow for the traversal of a given search space. Exploitative operators may induce behavior that uses the current location in the fitness landscape to move a solution towards a more optimal region in the neighboring search space. Fitness Distributions is a recent technique to assess the viability and quality of such variation operators. This technique is applied to the evolution of modular and non-modular finite state machines. Experiments are conducted on a number of tracking and control problems.
Discussion is directed towards assessing the overall effectiveness of operators for such machines.
Special attention is given to FD relations which show no local optimum in the correlation plot, as is the case for the relation induced by Horn's longpath. A tentative taxonomy is proposed for he different types of GA behavior found within a class of fitness functions with a common correlation plot. Most commonly, one or more randomly selected bits are flipped with some probability. The success of such a variation operator invariably depends on the location and number of bits being varied in the chromosome. Given an objective function and a candidate parent, bit mutations in some locations will provide an improvement in fitness whereas no improvement will result when mutations are applied to other locations.