Abstract:
The Learnable Evolution Model (LEM), first presented at the Fourth International Workshop on Multistrategy Learning, employs machine learing to guide evolutionary computation. Specifically, LEM integrates two modes of operation: Machine Learning mode, which employs a machine learning algorithm, and Darwinian Evolution mode, which employs a conventional evolutionary algorithm. The central new idea of LEM is that in machine learning mode, new individuals are "genetically engineered" by a repeated process of hypothesis formation and instantiation, rather than created by random operators of mutation and/or recombination, as in Darwinian-type evolutionary algorithms. At each stage of evoluation, hypotheses are induced by a machine learning system from examples of high and low performance individuals. New individuals are created by instantiating the hypotheses in different ways. In recent experiments concerned with complex function optimization problems, LEM has significantly outperformed selected evolutionary computation algorithms, sometimes achieving speed-ups of the evolutionary process by two or more orders of magnitude (in terms of the number of generations). In another recent application involving a problem of optimizing heat exchangers, LEM produced designs equal or superior to best expert designs. The recent results have confirmed earlier findings that LEM is able to significantly speed-up evolutionary processes (in terms of the number of generations) for certain problems. Further research is needed to determine classes of problems for which LEM is most advantagious.
