Recently, fitness distribution analysis have been proposed as an alternative tool for exploring operator behavior and designing efficient evolutionary computations. For example, the step size of a single parent variation operator determines the corresponding probability of finding better solutions and the expected improvement that will be obtained. This paper analyses the utility of Gaussian, Cauchy, and Mean mutation operators when a parent is located on a local extrema of a continuous objective function that is to be optimized. For a given representations in an evolutionary computation, there are a number of variation operators that can be applied to existing solutions to create new solutions. These variation operators can be classified into two broad categories, exploratory and exploitative operators.
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.