Self-Adaptation of Mutation Rates and Dynamic Fitness

Proceedings of the AAAI Conference on Artificial Intelligence, 13

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 13

AAAI-96 Student Abstracts

In any search via artificial evolution, the likelihood of stagnation at local optima is determined by the particular choices of representation and search operators. Because the performance impact of these design choices is difficult to predict, it is an attractive option to let the representation and/or operators themselves evolve. However, effective evolution at this meta-level can be difficult to achieve for a number of reasons, including: (1) The complexity of the search space is increased; and (2) selection acts at the level of the fitness function and only indirectly at the meta-level, favoring variations only to the extent to which they are stochastically associated with fitness improvements. The question then becomes: Under what conditions is evolution of the representation and/or operators likely to be most effective?