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Improving the Evolutionary Coding for Machine Learning Tasks
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. The coding of the search space for the obtaining of decision
rules is approached, i.e., the representation of the individuals of
the genetic population. Two new methods for encoding discrete and
continuous attributes are presented. Our “natural coding” uses one
gene per attribute (continuous or discrete) leading to a reduction in
the search space. Genetic operators for this approached natural coding
are formally described and the reduction of the size of the search
space is analysed for several databases from the UCI machine learning
repository.Comisión Interministerial de Ciencia y Tecnología TIC1143–C03–0
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