Estimation of distribution algorithms (EDA) are similar to genetic algorithms except that they replace crossover and mutation with sampling from an estimated probability distribution. We develop a framework for estimation of distribution algorithms based on the principle of maximum entropy and the conservation of schema frequencies. An algorithm of this type gives better performance than a standard genetic algorithm (GA) on a number of standard test problems involving deception and epistosis (i.e. Trap and NK). © Springer-Verlag Berlin Heidelberg 2004.
CITATION STYLE
Wright, A., Poli, R., Stephens, C., Langdon, W. B., & Pulavarty, S. (2004). An estimation of distribution algorithm based on maximum entropy. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3103, 343–354. https://doi.org/10.1007/978-3-540-24855-2_30
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