A Clustering Based Niching EA for Multimodal Search Spaces

Streichert, Felix; Stein, Gunnar; Ulmer, Holger; Zell, Andreas

doi:10.1007/978-3-540-24621-3_24

Cited by 41 publications

(32 citation statements)

References 11 publications

(10 reference statements)

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“…And finally, the population state management phase, which allows to balance the sub-population size if necessary, tracks the state of convergence for each sub-population and allows re-initialization of converged sub-populations while memorizing the obtained result in the list of identified global/local optima R. The final result of the CBN-EA is then the list of identified global/local optima R without any further post-processing. A more detailed description of the algorithm and its implementation can be found in publications from the authors [27].…”

Section: Clustering Based Niching Ea (Cbn)mentioning

confidence: 99%

Clustering-based Approach to Identify Solutions for the Inference of Regulatory Networks

Spieth¹,

Streichert²,

Speer³

et al.

2005 IEEE Congress on Evolutionary Computation

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Abstract-In this paper we address the problem of finding valid solutions for the problem of inferring gene regulatory networks. Different approaches to directly infer the dependencies of gene regulatory networks by identifying parameters of mathematical models can be found in literature. The problem of reconstructing regulatory systems from experimental data is often multimodal and thus appropriate optimization strategies become necessary. Thus, we propose to use a clustering based niching evolutionary algorithm to maintain diversity in the optimization population to prevent premature convergence and to raise the probability of finding the global optimum by identifying multiple alternative networks. With this set of alternatives, the identification of the true solution has then to be addressed in a second post-processing step.

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Section: Clustering Based Niching Ea (Cbn)mentioning

confidence: 99%

Clustering-based Approach to Identify Solutions for the Inference of Regulatory Networks

Spieth¹,

Streichert²,

Speer³

et al.

2005 IEEE Congress on Evolutionary Computation

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show abstract

“…g e t O p t i m i z e r ( ) ; 7 e s . s e t P l u s S t r a t e g y ( true ) ; // a c c e s s t h e ES and s e t a p l u s s e l e c t i o n s t r a t e g y niching ES is another interesting approach [2]. Checking o the post-processing option, the niching ES run delivers two dozen optima rened by local search.…”

Section: Use Casesmentioning

confidence: 99%

The EvA2 Optimization Framework

Kronfeld

Planatscher

Zell

2010

Lecture Notes in Computer Science

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Abstract. We present EvA2, a comprehensive metaheuristic optimization framework with emphasis on Evolutionary Algorithms. It presents a modular structure of interfaces and abstract classes for the implementation of both optimization problems and solvers. End users may choose among several layers of abstraction for an entrance point meeting their requirements on ease of use and access to extensive functionality. The EvA2 framework has been applied successfully in several academic as well as industrial cooperations and is extended continuously. It is freely available under an open source license (LGPL).

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“…Therefore, we decided to use clustering algorithms instead, to search for an suitable partitioning for each individual problem instance. Clustering algorithms have been used together with EAs searching for niches in multi-modal search spaces [19,16]. Therefore, it is most straightforward to apply cluster algorithms to pursue a 'divide and conquer' approach for parallel MOEAs in arbitrary dimensions.…”

Section: Clustering Based Parallelization Schemementioning

confidence: 99%

Parallelization of Multi-objective Evolutionary Algorithms Using Clustering Algorithms

Streichert

Ulmer

Zell

2005

Lecture Notes in Computer Science

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Abstract. While Single-Objective Evolutionary Algorithms (EAs) parallelization schemes are both well established and easy to implement, this is not the case for Multi-Objective Evolutionary Algorithms (MOEAs). Nevertheless, the need for parallelizing MOEAs arises in many real-world applications, where fitness evaluations and the optimization process can be very time consuming. In this paper, we test the 'divide and conquer' approach to parallelize MOEAs, aimed at improving the speed of convergence beyond a parallel island MOEA with migration. We also suggest a clustering based parallelization scheme for MOEAs and compare it to several alternative MOEA parallelization schemes on multiple standard multi-objective test functions.

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A Clustering Based Niching EA for Multimodal Search Spaces

Cited by 41 publications

References 11 publications

Clustering-based Approach to Identify Solutions for the Inference of Regulatory Networks

Clustering-based Approach to Identify Solutions for the Inference of Regulatory Networks

The EvA2 Optimization Framework

Parallelization of Multi-objective Evolutionary Algorithms Using Clustering Algorithms

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