A Fast Genetic Algorithm with Sharing Scheme Using Cluster Analysis Methods in Multimodal Function Optimization

Yin, Xiaodong; Germay, Noël

doi:10.1007/978-3-7091-7533-0_65

Cited by 168 publications

(87 citation statements)

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“…Please refer to the survey proposed in [23] (or to the original papers of course). A popular family of MMO algorithms is based on niching techniques: sharing [8]; clearing [22], including the modified clearing approach proposed in [23]; crowding [6], including deterministic [18] and probabilistic [19] versions; clustering [30]; species conserving genetic algorithms [16]; and finally, different restart strategies, to the recent state-of-the-art restart with increasing population size [1]. Several other works have been devoted to MMO outside the evolutionary community.…”

Section: Introductionmentioning

confidence: 99%

A Rigorous Runtime Analysis for Quasi-Random Restarts and Decreasing Stepsize

Schoenauer

Teytaud

2012

Lecture Notes in Computer Science

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Abstract. Multi-Modal Optimization (MMO) is ubiquitous in engineering, machine learning and artificial intelligence applications. Many algorithms have been proposed for multimodal optimization, and many of them are based on restart strategies. However, only few works address the issue of initialization in restarts. Furthermore, very few comparisons have been done, between different MMO algorithms, and against simple baseline methods. This paper proposes an analysis of restart strategies, and provides a restart strategy for any local search algorithm for which theoretical guarantees are derived. This restart strategy is to decrease some 'step-size', rather than to increase the population size, and it uses quasi-random initialization, that leads to a rigorous proof of improvement with respect to random restarts or restarts with constant initial step-size. Furthermore, when this strategy encapsulates a (1+1)-ES with 1/5th adaptation rule, the resulting algorithm outperforms state of the art MMO algorithms while being computationally faster.

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Section: Introductionmentioning

confidence: 99%

A Rigorous Runtime Analysis for Quasi-Random Restarts and Decreasing Stepsize

Schoenauer

Teytaud

2012

Lecture Notes in Computer Science

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“…Apart from the above, many more niching methods have been developed over the years, including derating [1], deterministic crowding [24], restricted tournament selection [15], parallelization [2], clustering [37], and speciation [30,20]. Niching methods have also been developed for PSO, such as NichePSO [31], SPSO [26], and VPSO [33].…”

Section: Niching Methodsmentioning

confidence: 99%

“…A classic example is the sharing parameter σ share in fitness sharing [14]. Other uses of niching parameters include crowding factor in crowding method [12], the window size w in restricted tournament selection [15], or the number of clusters in k-means clustering methods [37,17].…”

Section: Difficulties Facing Niching Methodsmentioning

confidence: 99%

“…For example the sharing parameter σ share in fitness sharing [14], the species distance σ s in species conserving GA (SCGA) [20], the distance measure σ clear in clearing [30], and the species radius r s in the speciation-based PSO (SPSO) [26]. Sometimes niching parameters can be under different disguises, such as the crowding factor in crowding [12], the window size w in restricted tournament selection [15], or the number of clusters in kmeans clustering methods [37,17]. The performance of these EAs depend very much on how these parameters are specified.…”

Section: Problems With Niching Parametersmentioning

confidence: 99%

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Developing Niching Algorithms in Particle Swarm Optimization

2011

Adaptation, Learning, and Optimization

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Abstract. Niching as an important technique for multimodal optimization has been used widely in the Evolutionary Computation research community. This chapter aims to provide a survey of some recent efforts in developing stateof-the-art PSO niching algorithms. The chapter first discusses some common issues and difficulties faced when using niching methods, then describe several existing PSO niching algorithms and how they combat these problems by taking advantages of the unique characteristics of PSO. This chapter will also describe a recently proposed lbest ring topology based niching PSO. Our experimental results suggest that this lbest niching PSO compares favourably against some existing PSO niching algorithms.

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“…Yin and Germay [21] introduced an Adaptive Clustering Algorithm (ACA) to avoid the a priori estimation of σ share . Instead of the original sharing function of equations (1) and (2) they used equations (3) and (4).…”

Section: Niching Evolutionary Algorithmsmentioning

confidence: 99%

A Clustering Based Niching EA for Multimodal Search Spaces

Streichert

Stein

Ulmer

et al. 2004

Lecture Notes in Computer Science

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Abstract. We propose a new niching method for Evolutionary Algorithms which is able to identify and track global and local optima in a multimodal search space. To prevent the loss of diversity we replace the global selection pressure within a single population by local selection of a multi-population strategy. The sub-populations representing species specialized on niches are dynamically identified using standard clustering algorithms on a primordial population. With this multi-population strategy we are able to preserve diversity within the population and to identify global/local optima directly without further post-processing.

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A Fast Genetic Algorithm with Sharing Scheme Using Cluster Analysis Methods in Multimodal Function Optimization

Cited by 168 publications

References 1 publication

A Rigorous Runtime Analysis for Quasi-Random Restarts and Decreasing Stepsize

A Rigorous Runtime Analysis for Quasi-Random Restarts and Decreasing Stepsize

Developing Niching Algorithms in Particle Swarm Optimization

A Clustering Based Niching EA for Multimodal Search Spaces

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