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Optimizing complex systems by intelligent evolotion: the LEMd method and case study

Michalski R.S.

Bulletin of the Polish Academy of Sciences. Technical Sciences

2006

Vol. 54, nr 4

505-513

Most methods of evolutionary computation follow a Darwinian-type model that proceeds through random mutations or recombinations of the genetic material and natural selection of individuals carried out according to the principle of the survival of the fittest. In such a model, the creation of new individuals is not guided by any reasoning process or "external mind", but rather by random or semi-random changes. Recently, a new, non-Darwinian approach to evolutionary computation bas been proposed, called Learnable Evolution Model (LEM), in which the evolutionary process is guided by computational intelligence. In LEM, a new way of creating individuals is proposed, namely, by hypothesis formation and instantiation. In numerous experiments, LEM bas consistently and significantly outperformed compared conventional Darwinian-type algorithms in terms of the evolution length (the number of fitness evaluations) in solving complex function optimization problems. Based on the LEM ideas, we developed a method, called LEMd, which is tailored to problems of optimizing very complex engineering systems. This article provides a brief description of LEMd and its application to the development of a specialized system, ISHED, for the optimization of evaporator designs in cooling systems. According to experts in cooling systems, ISHED-developed designs have matched or outperformed the best human designs. These results and those from the experimental testing of learnable evolution on problems with hundreds of variables suggest that LEMd may be an attractive new tool for optimizing very complex engineering systems.