Impatience Mechanism in Saddles' Crossing

摘要

Evolutionary inspired heuristics suffer from a premature convergence at local optima and, consequently, a population diversity loss. Thus, breaking out of a local optimum trap and crossing saddles between optima in multimodal and multidimensional search spaces is an important issue in an evolutionary optimization algorithm. In this paper, an impatience mechanism coupled with a phenotypic model of evolution is studied. This mechanism diversifies a population and facilitates escaping from a local optima trap. An impatient population polarizes itself and evolves as a dipole centered around an averaged individual. The operator was modified by supplying it with an extra knowledge about a currently found optimum. In the case, behavior of a population is quite different - a significant diversification is observed but the population is not polarized and evolves as a single cluster. Both mechanisms allow to cross saddle relatively fast for a wide range of parameters of a bimodal multidimensional fitness function.