Natural Encoding for Evolutionary Supervised Learning


One of the most influential factors in the quality of the solutions found by an evolutionary algorithm are a correct coding of the search space and an appropriate evaluation function of the potential solutions. Evolutionary algorithms are often used to learn decision rules from datasets, which are encoded as individuals in the genetic population. In this paper, the coding of the search space for the obtaining of those decision rules is approached, i.e., the representation of the individuals of the genetic population and also the design of specific genetic operators.

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