Machine Discovery and Machine Learning Based on Adaptation and Evolution

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Research results through two years are summarized as follows ;(1) Research results in evolutionary computation1) An analysis on the role of crossover operators in genetic algorithmsWe made a mathematical analysis on the effect of crossover operators to search better solutions with genetic algorithms, and showed the "deceptive boundary theorem."2) A proposal of evaluation criteria on a coding-crossoverWe proposed four criteria of completeness, soundness, non-redundancy and character preservingness to evaluate a model formulation.3) A proposal and applications of the subtour exchange crossover as a character preserving operationWe proposed a character preserving operation named the subtour exchange crossover in the domain of ordering problems.4) A proposal of genetic algorithms for multiobjective optimization problemsWe established a methodology for genetic algorithms to generate the pareto optimal set, which is the rational solution for a multiobjective optimization.(2) Research results … More in adaptive computation1) A proposal of a framework and a categorization for reinforcement learningWe defined a transparent framework for reinforcement learning, and classified existing researches according to the class of the environment and the orientation of the approach.2) An analysis on the rationality of the exploitation oriented reinforcement learningWe made a mathematical analysis on the rationality of the sharing functions in the profit sharing method, and showed two kinds of rationality theorem.3) A proposal and an extention of the k-certain exploration methodWe proposed a exploration oriented action selection strategy named the k-certain exploration method to efficiently identify an unknown environment.4) A proposal of a reinforcement learning method based on a hillclimbing of the expected rewardsWe proposed an incremental reinforcement learning method, which performs a hillclimbing along the gradient of the expected rewards.5) A proposal of a learning method under perceptual aliasingWe proposed a prediction model to foresee the state transition under incomplete deceptive perception, and developed a learning method to construct a prediction model with trial and error. Less