Assessing operator effectiveness on finite state machines using fitness distributions

Given a representation in an evolutionary computation method, there are a number of variation operators that can be applied to extant solutions in the population to create new solutions. These variation operators can generally be classified into two broad categories, exploratory and exploitative operators. While exploratory operators allow for the traversal of a given search space, exploitative operators induce behavior that causes the solution to move towards nearby locally optimal points on the fitness landscape. Fitness distribution analysis is a recent technique for assessing the reliability and quality of variation operators in light of an objective function to be optimized. This technique is applied to the evolution of modular and non-modular finite state machines. Experiments are conducted on two instances of a tracking problem. Discussion is directed towards assessing the overall effectiveness of operators for such machines. The effect of the employed operators is consistent with previous intuitions when non-modular FSMs are used. Experiments using modular FSMs indicate a more exploratory nature for the employed variation operators. Results indicate a high degree of sensitivity to the employed variation operators when applied to modular FSMs.