GA with Exaptation: New Algorithms to Tackle Dynamic Problems

摘要

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.