The Kullback-Leibler Divergence of gene distributions
between successive generations of the Extended Compact
Genetic Algorithm (ECGA) is explored. Therein, the fragility
of the algorithm’s dependability to the beginning generations’
biasing is suggested. A novel approach within the scope of the
ECGA for choosing a better bias by allowing the ECGA to
simulate itself is presented. It is shown that, by simulating itself,
the ECGA is able to use a smaller population and evaluate fewer
fitness calls while maintaining the same ability to find optimal
A Kullback-Leibler Divergence Exploration into a Look-Ahead Simulation Optimization of the Extended Compact Genetic Algorithm.
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