The crowding approach to niching in genetic algorithms

A wide range of niching techniques have been investigated in evolutionary and ge-netic algorithms. In this article, we focus on niching using crowding techniques in the context of what we call local tournament algorithms. In addition to determinis-tic and probabilistic crowding, the family of local tournament algorithms includes the Metropolis algorithm, simulated annealing, restricted tournament selection, and par-allel recombinative simulated annealing. We describe an algorithmic and analytical framework which is applicable to a wide range of crowding algorithms. As an ex-ample of utilizing this framework, we present and analyze the probabilistic crowding niching algorithm. Like the closely related deterministic crowding approach, proba-bilistic crowding is fast, simple, and requires no parameters beyond those of classical genetic algorithms. In probabilistic crowding, sub-populations are maintained reliably, and we show that it is possible to analyze and predict how this maintenance takes place. We also provide novel results for deterministic crowding, show how different crowding replacement rules can be combined in portfolios, and discuss population siz-ing. Our analysis is backed up by experiments that further increase the understanding of probabilistic crowding