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This paper extends the genetic algorithm with neutral mutations in oder to trace the complex optimal path in dynamic combinatorial optimization problems, by utilizing the unique characteristics of the method. That is to say, ( 1 ) the search performance does not deteriorate seriously if the population converges, and ( 2 ) directed evolution is observed after sufficient generations have passed. Considering the second characteristic to be crucial to these problems, we extend the GA to emphasize the development of the directions, by adding the temporal phenotype into the genotype as fixation operation and applying a stochastic learning automaton (SLA) on these fixed points to select one of them as the phenotype. To ensure the effectiveness of the proposed method, computer simulations are conducted. The results show that the proposed method generates appropriate automata and traces complex optimal paths of dynamic problems.

Adaptation to a Dynamic Environment by Using GA and SLA