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- W838874927 abstract "Genetic Algorithms(GAs) are stochastic computational models inspired by natural phenomena. Twomajor procedures of GAs, therefore, are operation and selection. By means of operation such as crossover, mutation, inversion, etc., the individuals can produce new offsprings, and through the selection mechanism the fitter ones can produce more offspring than the less fit ones. GAs, however, have some risks to 1111 into local minima because their control parameters such as population size, crossover rate, and mutation probability, can not be obtained deterministically but heuristically. We, therefore, propose an adaptive genetic algorithm(AGA), which generate mutation probabilities of each locus by interacting with the environment according to reinforcement learning. We verify the effectiveness of the proposed method via several simulation results compared with simple GAs." @default.
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- W838874927 date "1999-01-01" @default.
- W838874927 modified "2023-09-23" @default.
- W838874927 title "Adaptive Genetic Algorithms Using Reinforcement Learning" @default.
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