Winnowing Wheat from Chaff: The Chunking GA

Stringer, Hal; Wu, Annie S.

doi:10.1007/978-3-540-24855-2_18

Cited by 7 publications

(6 citation statements)

References 11 publications

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“…Other works interested on using this idea can be found within evolutionary computation. For instance, [25] integrated this idea into a variable-length genetic algorithm (GA) (though ignoring the earlier work of [23]). The basic idea of this GA variant is to use an increasing length of chromosomes in order to solve progressively the optimization problem through solving some smaller subproblems.…”

Section: Chunkingmentioning

confidence: 99%

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Chunking and cooperation in particle swarm optimization for feature selection

Sarhani

Voß

2021

Ann Math Artif Intell

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Bio-inspired optimization aims at adapting observed natural behavioral patterns and social phenomena towards efficiently solving complex optimization problems, and is nowadays gaining much attention. However, researchers recently highlighted an inconsistency between the need in the field and the actual trend. Indeed, while nowadays it is important to design innovative contributions, an actual trend in bio-inspired optimization is to re-iterate the existing knowledge in a different form. The aim of this paper is to fill this gap. More precisely, we start first by highlighting new examples for this problem by considering and describing the concepts of chunking and cooperative learning. Second, by considering particle swarm optimization (PSO), we present a novel bridge between these two notions adapted to the problem of feature selection. In the experiments, we investigate the practical importance of our approach while exploring both its strength and limitations. The results indicate that the approach is mainly suitable for large datasets, and that further research is needed to improve the computational efficiency of the approach and to ensure the independence of the sub-problems defined using chunking.

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Section: Chunkingmentioning

confidence: 99%

“…That is, works interested in evolutionary optimization (e.g. [25]) often do not cite previous works on local search (e.g. [23]).…”

Section: Chunkingmentioning

confidence: 99%

Chunking and cooperation in particle swarm optimization for feature selection

Sarhani

Voß

2021

Ann Math Artif Intell

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“…Stringer [5], has handled bloat by explicitly setting an upper bound on the depth of evolved trees or by incorporating a parsimony pressure that adjusts the fitness of individuals by a tradeoff between performance and size. Bleuler, Brack, Thiele, and Zitzler proposed a nonparametric method, Double Tournament [8], this method is similar to a multi objective approach to bloat, however the objectives of fitness and size are treated separately.…”

Section: Previous Work Done In the Fieldmentioning

confidence: 99%

Controlling The Problem Of Bloating Using Stepwise Crossover And Double Mutation Technique

Bhardwaj¹,

Sakalle²,

Chouhan³

et al. 2011

ACIJ

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“…The ChGA appears able to separate good genes from bad (or less good) and store them in the shared memory structure. In [8] the authors provide a more formal investigation of this "winnowing" effect and the relationship between memory capacity and solution size.…”

Section: Introductionmentioning

confidence: 99%