MEDAL Report 2010003
Network crossover performance on NK landscapes and deceptive problems
Mark W Hauschild and Martin Pelikan  (2010)

Abstract. By being able to build and sample models of promising solutions, Estimation of Distribution Algorithms (EDAs) gain many advantages over standard evolutionary algorithms. However, this comes at the cost of an expensive model-building phase. One way to try and eliminate this bottleneck is to incorporate prior knowledge into the model-building, but this can be a difficult task. Another approach is to modify the crossover operator in a standard genetic algorithm(GA) to exploit this prior information. This paper describes a method to build a network crossover operator that can be used in a GA to easily incorporate problem-specific knowledge such that it better respects linkages between bits. The performance of this operator in the simple genetic algorithm(GA) is then compared to other operators as well as the hierarchical Bayesian Optimization Algorithm (hBOA) on several different problem types, all with both elitism replacement and Restricted Tournament Replacement (RTR). The performance of all the algorithms and replacement mechanisms are then analyzed and the results are discussed.

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Missouri Estimation of Distribution Algorithms Laboratory
Department of Mathematics and Computer Science
University of Missouri-St. Louis, St. Louis, MO


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