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This paper concerns applications of genetic algorithms and geneticprogramming to tasks for which it is difficult to find a representation thatdoes not map to a highly complex and discontinuous fitness landscape. In suchcases the standard algorithm is prone to getting trapped in local extremes. Thepaper proposes several adaptive mechanisms that are useful in preventing thesearch from getting trapped.

Fitness-based Adaptive Control of Parameters in Genetic Programming: Adaptive Value Setting of Mutation Rate and Flood Mechanisms