Abstract
Several optimisation problems have a highly symmetric solution space. In this case, there exist multiple equivalent solutions with respect to their structure and cost. Genetic algorithms do not generally perform well in this domain due to the lack of an effective search. In fact, the algorithm is very likely to explore a relatively small part of the space without being able to detect that a solution has been previously evaluated. As a consequence, the algorithm either converges to poor solutions or does not converge at all. This paper introduces two ad hoc genetic operators to deal directly with highly symmetric solution spaces. Both operators replace the standard crossover procedure of genetic algorithms. We present results of an investigation on combinatorial optimisation problems and in particular the graph colouring problem.
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Marino, A. (2001). Genetic Search on Highly Symmetric Solution Spaces: Preliminary Results. In: Kallel, L., Naudts, B., Rogers, A. (eds) Theoretical Aspects of Evolutionary Computing. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04448-3_19
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DOI: https://doi.org/10.1007/978-3-662-04448-3_19
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