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)
CWI
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.