Abstract
In GAs applied to engineering problems (in our case, the optimisation of logic circuits) the fitness function is usually complex and the fitness evaluation is time consuming. The run time is therefore a major consideration when designing a GA for optimisation, thus a look-up table for fitness evaluation is desirable. As a consequence, it is appropriate to limit the number of different chromosome fitness evaluations that any particular run of the GA will be allowed to examine. In this situation the user is uninterested in the number of generations required. It is necessary in this approach to guarantee the users that they will be able to find a good and reliable problem solution within the limited number of evaluations, and hence time available. We refer to this type of GA as a restricted evaluation GA. In this paper we suggest a number of hybrid algorithms which combine a GA with a neighbourhood search (TABU) technique to provide this performance and reliability. The effectiveness of each of these methods is compared and contrasted, and underlying principles are suggested as to why these techniques might prove to be useful in these types of problem.
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© 1996 Springer-Verlag Berlin Heidelberg
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Miller, J.F., Thomson, P. (1996). Restricted evaluation genetic algorithms with Tabu search for optimising Boolean functions as multi-level AND-EXOR networks. In: Fogarty, T.C. (eds) Evolutionary Computing. AISB EC 1996. Lecture Notes in Computer Science, vol 1143. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0032775
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DOI: https://doi.org/10.1007/BFb0032775
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