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Selected Works

Ole J Mengshoel

Deterministic crowding

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Full-Text Articles in Engineering

Generalized Crowding For Genetic Algorithms, Ole J. Mengshoel, Severino F. Galan Jun 2010

Generalized Crowding For Genetic Algorithms, Ole J. Mengshoel, Severino F. Galan

Ole J Mengshoel

Crowding is a technique used in genetic algorithms to preserve diversity in the population and to prevent premature convergence to local optima. It consists of pairing each offspring with a similar individual in the current population (pairing phase) and deciding which of the two will remain in the population (replacement phase). The present work focuses on the replacement phase of crowding, which usually has been carried out by one of the following three approaches: Deterministic, Probabilistic, and Simulated Annealing. These approaches present some limitations regarding the way replacement is conducted. On the one hand, the first two apply the same …

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The Crowding Approach To Niching In Genetic Algorithms, Ole J. Mengshoel, David E. Goldberg Dec 2007

The Crowding Approach To Niching In Genetic Algorithms, Ole J. Mengshoel, David E. Goldberg

Ole J Mengshoel

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 …

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Probabilistic Crowding: Deterministic Crowding With Probabilistic Replacement, Ole J. Mengshoel, David E. Goldberg Jun 1999

Probabilistic Crowding: Deterministic Crowding With Probabilistic Replacement, Ole J. Mengshoel, David E. Goldberg

Ole J Mengshoel

This paper presents a novel niching algorithm, probabilistic crowding. Like its predecessor deterministic crowding, probabilistic crowding is fast, simple, and requires no parameters beyond that of the classical GA. In probabilistic crowding, subpopulations are maintained reliably, and we analyze and predict how this maintenance takes place.

This paper also identifies probabilistic crowding as a member of a family of algorithms, which we call integrated tournament algorithms. Integrated tournament algorithms also include deterministic crowding, restricted tournament selection, elitist recombination, parallel recombinative simulated annealing, the Metropolis algorithm, and simulated annealing.

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