Hisashi Tamaki

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This paper introduces a coevolutionary approach to genetic algorithms (GAs) for exploring not only wihtin a part of the solution space defined by the genotype-phenotype map but also the map itself. In canonical GAs with the fixed map, how large area of the solution space can be covered by possible genomes and consequently how better solutions can be found(More)
Engineers and researchers are paying more attention to reinforcement learning (RL) as a key technique for realizing adaptive and autonomous decentralized systems. In general, however, it is not easy to put RL into practical use. Our approach mainly deals with the problem of designing state and action spaces. Previously, an adaptive state space construction(More)