Abstract
This work presents an analysis of the static Aging operator for different evolutionary algorithms: two immunological algorithms (OptIA and Clonalg), a standard genetic algorithm SGA, and Differential Evolution (DE) algorithm. The algorithms were tested against standard benchmarks in both unconstrained and dynamic optimisation problems. This work analyses whether the aging operator improves the results when applied to evolutionary algorithms. With the exception of DE, the results report that every algorithm shows an improvement in performance when used in combination with Aging.
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Castrogiovanni, M., Nicosia, G., Rascuná, R. (2007). Experimental Analysis of the Aging Operator for Static and Dynamic Optimisation Problems. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4694. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74829-8_98
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DOI: https://doi.org/10.1007/978-3-540-74829-8_98
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