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EC
2006

A Step Forward in Studying the Compact Genetic Algorithm

14 years 8 hour ago
A Step Forward in Studying the Compact Genetic Algorithm
The compact Genetic Algorithm (cGA) is an Estimation of Distribution Algorithm that generates offspring population according to the estimated probabilistic model of the parent population instead of using traditional recombination and mutation operators. The cGA only needs a small amount of memory; therefore, it may be quite useful in memory-constrained applications. This paper introduces a theoretical framework for studying the cGA from the convergence point of view in which, we model the cGA by a Markov process and approximate its behavior using an Ordinary Differential Equation (ODE). Then, we prove that the corresponding ODE converges to local optima and stays there. Consequently, we conclude that the cGA will converge to the local optima of the function to be optimized. Keywords Compact genetic algorithm, Markov process, weak convergence, ordinary differential equation, stationary configuration, stability.
Reza Rastegar, Arash Hariri
Added 12 Dec 2010
Updated 12 Dec 2010
Type Journal
Year 2006
Where EC
Authors Reza Rastegar, Arash Hariri
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