Abstract
A stagnation of evolutionary search is frequently associated with missing population diversity. The resulting degradation of the overall performance can be avoided by applying methods for diversity management. This paper introduces a conceptually simple approach to maintain diversity called partial restart. The basic idea is to re-initialize parts of the population after certain time intervals, thereby raising the probability of escaping from local optima that have dominated the recent search progress. The usefulness of the proposed technique is evaluated empirically in two characteristic problem domains, represented by the satisfiability problem and the onemax problem. The main goal is to identify problem structures where partial restarts are promising, and to gain a better understanding of the relations between different variants of partial restarts.
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Tendresse, I.l., Gottlieb, J., Kao, O. (2002). The Effects of Partial Restarts in Evolutionary Search. In: Collet, P., Fonlupt, C., Hao, JK., Lutton, E., Schoenauer, M. (eds) Artificial Evolution. EA 2001. Lecture Notes in Computer Science, vol 2310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46033-0_10
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DOI: https://doi.org/10.1007/3-540-46033-0_10
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