Towards a generally applicable self-adapting hybridization of evolutionary algorithms

Abstract

Practical applications of Evolutionary Algorithms (EA) frequently use some sort of hybridization by incorporating domain-specific knowledge, which turns the generally applicable EA into a problem-specific tool. To overcome this limitation, the new method of HyGLEAM was developed and tested extensively using eight test functions and three real-world applications. One basic kind of hybridization turned out to be superior and the number of evaluations was reduced by a factor of up to 100. © Springer-Verlag Berlin Heidelberg 2004.