Multi-parent recombination to overcome premature convergence in genetic algorithms
Recent research shows that enlarging the arity of recombination operators in a Genetic Algorithm lowers the probability of premature convergence. This results in more robust genetic function optimizers. In this paper we try to give an explanation why these multi-parent operators are better. In particular, we discuss two operators: the uniform scanning crossover and the diagonal crossover operator. First we show that these operators are better than the standard ones by testing them on an extensive test-suite of function optimization problems. Second, we explain the empirical results by first looking at the influence that the operators have on the evolution of populations, and then by using a new kind of description we are able to explain the convergence curves and rates of success as obtained in the experiments.
|Ordinary Differential Equations (acm G.1.7), Problem Solving, Control Methods, and Search (acm I.2.8)|
|Problem solving (heuristics, search strategies, etc.) (msc 68T20)|
|Department of Computer Science [CS]|
van Kemenade, C.H.M, & Eiben, A.E. (1995). Multi-parent recombination to overcome premature convergence in genetic algorithms. Department of Computer Science [CS]. CWI.