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Benefits of a population: five mechanisms that advantage population-based algorithms

Prugel-Bennett, A. (2010) Benefits of a population: five mechanisms that advantage population-based algorithms IEEE Transactions on Evolutionary Computation, 14, (4), pp. 500-517. (doi:10.1109/TEVC.2009.2039139).

Record type: Article


This paper identifies five distinct mechanisms by which a population-based algorithm might have an advantage over a solo-search algorithm in classical optimization. These mechanisms are illustrated through a number of toy problems. Simulations are presented comparing different search algorithms on these problems. The plausibility of these mechanisms occurring in classical optimization problems is discussed. The first mechanism we consider relies on putting together building blocks from different solutions. This is extended to include problems containing critical variables. The second mechanism is the result of focusing of the search caused by crossover. Also discussed in this context is strong focusing produced by averaging many solutions. The next mechanism to be examined is the ability of a population to act as a low-pass filter of the landscape, ignoring local distractions. The fourth mechanism is a population's ability to search different parts of the fitness landscape, thus hedging against bad luck in the initial position or the decisions it makes. The final mechanism is the opportunity of learning useful parameter values to balance exploration against exploitation.

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e-pub ahead of print date: 29 March 2010
Published date: August 2010
Organisations: Southampton Wireless Group


Local EPrints ID: 270918
PURE UUID: 2d82ef47-5b0d-4471-bdee-354a838613b0

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Date deposited: 23 Apr 2010 14:13
Last modified: 18 Jul 2017 06:49

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Author: A. Prugel-Bennett

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