Sushil J. Louis, Judy Johnson
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.