Abstract
A wide range of niching techniques have been investigated in evolutionary and genetic algorithms. In this article, we focus on niching using crowding techniques in the context of what we call local tournament algorithms. In addition to deterministic and probabilistic crowding, the family of local tournament algorithms includes the Metropolis algorithm, simulated annealing, restricted tournament selection, and parallel recombinative simulated annealing. We describe an algorithmic and analytical framework which is applicable to a wide range of crowding algorithms. As an example of utilizing this framework, we present and analyze the probabilistic crowding niching algorithm. Like the closely related deterministic crowding approach, probabilistic crowding is fast, simple, and requires no parameters beyond those of classical genetic algorithms. In probabilistic crowding, subpopulations 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 sizing. Our analysis is backed up by experiments that further increase the understanding of probabilistic crowding.
Highlights
Niching algorithms and techniques constitute an important research area in genetic and evolutionary computation
We briefly discuss similarities and differences between CROWDINGSTEP and SIMPLESTEP. Their overall structure is similar: First, one or more parents are selected from the population, one or more variation operators are applied, and similar individuals compete in local tournaments
Inspired by multimodal fitness functions and deterministic crowding (Mahfoud, 1995), we have investigated crowding in genetic algorithms, and in particular the probabilistic crowding approach
Summary
Niching algorithms and techniques constitute an important research area in genetic and evolutionary computation. The two main objectives of niching algorithms are (i) to converge to multiple, highly fit, and significantly different solutions, and (ii) to slow down convergence in cases where only one solution is required. Our main focus here is on crowding, and in particular we take as starting point the crowding approach known as deterministic crowding (Mahfoud, 1995). Strengths of deterministic crowding are that it is simple, fast, and requires no parameters in addition to those of a c 200X by the Massachusetts Institute of Technology. Deterministic crowding has been found to work well on test functions as well as in applications
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