On the application of cohort-driven operators to continuous optimization problems using evolutionary computation

Traditional approaches to real-valued function opt imization using evolutionary computational methods tend to use eith er self-adaptive operators (as in the case of evolutionary programming), or po pulation-based operators (as in the case of most real-valued genetic algorithms) . However, in general, most population-based operators are limited in scope to the use of at most two or three parent individuals. In this paper we explore an alternative population- based form of adaptation for evolutionary computati on, Guided Gaussian Mutation (GGM), which is designed specifically as a localized search operator. This operator is the first of a larger class of Coh ort Driven Operators (CDOs) which we define here. Experimental results using G GM in a standard genetic algorithm framework on a series of test problems sh ow impressive improvement over standard evolutionary programming.