Physics – Condensed Matter – Disordered Systems and Neural Networks
Scientific paper
2009-01-26
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2009), ACM Press, 843-850
Physics
Condensed Matter
Disordered Systems and Neural Networks
Scientific paper
10.1145/1569901.1570017
This paper provides an in-depth empirical analysis of several evolutionary algorithms on the one-dimensional spin glass model with power-law interactions. The considered spin glass model provides a mechanism for tuning the effective range of interactions, what makes the problem interesting as an algorithm benchmark. As algorithms, the paper considers the genetic algorithm (GA) with twopoint and uniform crossover, and the hierarchical Bayesian optimization algorithm (hBOA). hBOA is shown to outperform both variants of GA, whereas GA with uniform crossover is shown to perform worst. The differences between the compared algorithms become more significant as the problem size grows and as the range of interactions decreases. Unlike for GA with uniform crossover, for hBOA and GA with twopoint crossover, instances with short-range interactions are shown to be easier. The paper also points out interesting avenues for future research.
Katzgraber Helmut G.
Pelikan Martin
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