Abstract
Presents three novel techniques for enhancing the power of a genetic algorithm (GA) used to design fuzzy systems: a new context-dependent coding (CDC) technique, a simple chromosome reordering operator to maximize efficiency, and the coevolution of controller set tests to force competence in all areas of state space. These measures are shown to lead to a considerable improvement over conventional GAs when used to design controllers for a standard problem, such as the cart-pole problem. We use an analysis of GAs by L. Altenberg (1994) to determine a performance measure that demonstrates that our coding scheme and reordering operator improve the ability of the GA to organize itself and evolve chromosomal structures that not only produce high scores, but improve the search efficiency of the genetic operators. We investigate the algorithm in a controller to provide parallel parking maneuvers for mobile robots. It is shown that the controllers developed are robust to the systematic errors that inevitably arise when controllers are transferred from a simulated environment to the real world.
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