Diversity control based on population heterozygosity dynamics

Gouvêa, Maury Meirelles; Araújo, A.F.R.

doi:10.1109/cec.2008.4631295

Cited by 7 publications

(5 citation statements)

References 36 publications

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“…Unlike the first version of DRAC [6], this new version, DRAC v2, uses a population dynamics model [7] to adjust the controller and genetic parameters. This new approach benefits adaptive capacity because it considers the error between the population and model-reference diversity and the sensitivity of the performance criteria with respect to the control variables.…”

Section: Resultsmentioning

confidence: 99%

“…., x ni), its height Hi, and its slope Ri, and f2(x * ) = 300. For each change, moving peaks produces a randomly generated morphology in which random values for each cone are assigned based on specified ranges H i ∈ [0, 300], Ri ∈ [2,7], x ij ∈ [−100, 100], ∀i = 1, . .…”

Section: Non-continuous Environmentmentioning

confidence: 99%

“…In the first version of DRAC [6,7], the evolutionary process is regarded as a black box. Thus, there is not a model to describe the evolutionary process mathematically.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive evolutionary algorithm based on population dynamics for dynamic environments

Gouvêa

Araújo

2011

Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation

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In dynamic environments, the absence of diversity may degrade the performance of evolutionary algorithms (EAs). In a previous article, we introduced an method, diversityreference adaptive control (DRAC), to control population diversity based on reference diversity. DRAC aims to track an appropriate diversity level through a control-based strategy. In such a strategy, the evolutionary process is seen as a control problem, in which the process output is the population diversity and the process input is one or more EA adjustable parameters. In that first version of DRAC, the evolutionary process is treated as a black box, thus, the updating of the control variables is made as a function of the error between the population diversity and the referencemodel diversity. The DRAC approach does not consider sensitivity analysis. In the current version, a population dynamics model is used to describe the evolutionary process and to allow the control variables updating.

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Section: Resultsmentioning

confidence: 99%

Section: Non-continuous Environmentmentioning

confidence: 99%

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Adaptive evolutionary algorithm based on population dynamics for dynamic environments

Gouvêa

Araújo

2011

Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation

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“…The first type is the measurement based on the distance between particles [ 24 , 25 ]. This distance can be based on a central particle [ 26 ], the maximum distance between two particles in space [ 27 , 28 ], or the average distance between particles [ 29 ]. Euclidean distance is a common calculation method, because Euclidean space extends two-dimensional or three-dimensional space to any dimension, and at higher dimensions, populations exist and are defined in Euclidean space.…”

Section: Diversity Model Based On Dimension Entropymentioning

confidence: 99%

A Diversity Model Based on Dimension Entropy and Its Application to Swarm Intelligence Algorithm

Kang

Bei

Shen

et al. 2021

Entropy

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The swarm intelligence algorithm has become an important method to solve optimization problems because of its excellent self-organization, self-adaptation, and self-learning characteristics. However, when a traditional swarm intelligence algorithm faces high and complex multi-peak problems, population diversity is quickly lost, which leads to the premature convergence of the algorithm. In order to solve this problem, dimension entropy is proposed as a measure of population diversity, and a diversity control mechanism is proposed to guide the updating of the swarm intelligence algorithm. It maintains the diversity of the algorithm in the early stage and ensures the convergence of the algorithm in the later stage. Experimental results show that the performance of the improved algorithm is better than that of the original algorithm.

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“…Moreover, the gene frequency combination among all the landscape variables must be defined. For instance, in (Gouvêa Jr. & Araújo, 2008), a representative gene was preferred over averaging the diversity contribution of each gene (Wineberg & Oppacher, 2003).…”

Section: Introductionmentioning

confidence: 99%

Review and Study of Genotypic Diversity Measures for Real-Coded Representations

Corriveau

Guilbault

Tahan

et al. 2012

IEEE Trans. Evol. Computat.

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A B S T R A C TNumerous genotypic diversity measures (GDMs) are available in the literature to assess the convergence status of an evolutionary algorithm (EA) or describe its search behavior. In a recent study, the authors of this paper drew attention to the need for a GDM validation framework. In response, this study proposes three requirements (monotonicity in individual varieties, twinning, and monotonicity in distance) that can clearly portray any GDMs. These diversity requirements are analysed by means of controlled population arrangements. In this paper four GDMs are evaluated with the proposed validation framework. The results confirm that properly evaluating population diversity is a rather difficult task, as none of the analysed GDMs complies with all the diversity requirements.

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Diversity control based on population heterozygosity dynamics

Cited by 7 publications

References 36 publications

Adaptive evolutionary algorithm based on population dynamics for dynamic environments

Adaptive evolutionary algorithm based on population dynamics for dynamic environments

A Diversity Model Based on Dimension Entropy and Its Application to Swarm Intelligence Algorithm

Review and Study of Genotypic Diversity Measures for Real-Coded Representations

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