Abstract
It is propose new evolutionary algorithms with exaptive properties to tackle dynamic problems. Exaptation is a new theory with two implicit procedures of retention and reuse of old solutions. The retention of a solution involves some kind of memory and the reuse of a solution implies the adaptation of the solution to the new problem. The first algorithm proposed uses seeding techniques to reuse a solution and the second algorithm proposed uses memory with seeding techniques to retain and reuse solutions respectively. Both algorithms are compared with a simple genetic algorithm (SGA) and the SGA with two populations, where the first one is a memory of solutions and the second population is searching new solutions. The Moving Peak Benchmark (MPB) was used to test every algorithm.
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Torres-T, L. (2004). GA with Exaptation: New Algorithms to Tackle Dynamic Problems. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds) MICAI 2004: Advances in Artificial Intelligence. MICAI 2004. Lecture Notes in Computer Science(), vol 2972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24694-7_77
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DOI: https://doi.org/10.1007/978-3-540-24694-7_77
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