Robustness of Case-Initialized Genetic Algorithms

May 1999

Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference (FLAIRS 1999)

Volume

Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference (FLAIRS 1999)

All Papers

We investigate the robustness of Case Initialized Genetic AlgoRithm (CIGAR) systems with respect to problem indexing. When confronted with a series of similar problems CIGAR stores potential solutions in a case-base or an associative memory and retrieves and uses these solutions to help improve a genetic algorithm’s performance over time. Defining similarity among the problems, or indexing, is key to performance improvement. We study four indexing schemes on a class of simple problems and provide empirical evidence of CIGAR’s robustness to imperfect indexing.