Sequential versus Parallel Cooperative Coevolutionary Algorithms for Optimization

Popovici, Elena; Jong, Kenneth De

doi:10.1109/cec.2006.1688501

Cited by 12 publications

(10 citation statements)

References 7 publications

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“…For the dynamics analysis we used the technique previously employed in [10], [8], [7] and [9] and extended it by defining a quantitative metric for characterizing trajectories. The best-response problem property and the dynamics analysis of best individuals thus proved useful for understanding the behavior and optimization power of a wide range of variations of the basic coevolutionary algorithm [11].…”

Section: Discussionmentioning

confidence: 99%

“…The best of run essentially has the same distribution as the best of the first epoch, namely uniform over the interval [10,20]. Increasing epoch size can't make things worse than they already are.…”

Section: Dynamics Analysismentioning

confidence: 98%

“…In [4] run-time analysis was used to investigate the performance effects of update timing (i.e., whether the populations evolve simultaneously or take turns). The same parameter was analyzed in a different context in [10]. An initial study on the performance effects of population size and elitism was performed in [8] and then further extended in [9].…”

Section: Introductionmentioning

confidence: 99%

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The effects of interaction frequency on the optimization performance of cooperative coevolution

Popovici

Jong

2006

Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation

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Cooperative coevolution is often used to solve difficult optimization problems by means of problem decomposition. Its performance on this task is influenced by many design decisions. It would be useful to have some knowledge of the performance effects of these decisions, in order to make the more beneficial ones. In this paper we study the effects on performance of the frequency of interaction between populations. We show them to be problem-dependent and use dynamics analysis to explain this dependency.

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Section: Discussionmentioning

confidence: 99%

Section: Dynamics Analysismentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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The effects of interaction frequency on the optimization performance of cooperative coevolution

Popovici

Jong

2006

Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation

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“…CCEAs traditionally have been sequential, but we found that parallel update timing performs better (echoed by [21]). …”

Section: Cooperative Coevolutionary Algorithmsmentioning

confidence: 92%

Do multiple trials help Univariate methods?

Rothman

Luke

Sullivan

2011

2011 IEEE Congress of Evolutionary Computation (CEC)

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Cooperative Coevolutionary Algorithms (CCEAs) and Univariate Estimation of Distribution Algorithms (Univariate EDAs) are closely related algorithms in that both update marginal distributions/populations, and test samples of those distributions/populations by grouping them with collaborators drawn from elsewhere to form a complete solution. Thus the quality of these samples is context-sensitive and the algorithms assume low linkage among their variables. This results in well-known difficulties with these methods. While EDAs have commonly overcome these difficulties by examining multivariate linkage, CCEAs have instead examined basing the fitness of each marginal sample on the maximum of several trials. In this study we examine whether multiple-trial CCEA approach is really effective for difficult problems and large numbers of subpopulations; and whether this approach can be used to improve Univariate EDAs as well.

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“…The most common answers are to manage them by using either a sequential scheme or a parallel scheme. Several studies have been done comparing both approaches [71]. The assignation of fitness to each individual is also an open question.…”

Section: Coevolutionary Algorithmsmentioning

confidence: 99%

IFS-CoCo: Instance and feature selection based on cooperative coevolution with nearest neighbor rule

Derrac

García

Herrera

2010

Pattern Recognition

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Sequential versus Parallel Cooperative Coevolutionary Algorithms for Optimization

Cited by 12 publications

References 7 publications

The effects of interaction frequency on the optimization performance of cooperative coevolution

The effects of interaction frequency on the optimization performance of cooperative coevolution

Do multiple trials help Univariate methods?

IFS-CoCo: Instance and feature selection based on cooperative coevolution with nearest neighbor rule

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