When and what kind of memetic algorithms perform well

Lin, Jih-Yiing; Chen, Ying-ping

doi:10.1109/cec.2012.6252894

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…The purpose of an LS algorithm is to improve the current solution to the local optimum by exploiting the local neighborhood of a solution. The success of an LS algorithm depends on many things: the aptitude of the LS algorithm to exploit the neighborhood, the frequency and the length of the LS, the quality of the individual undergoing an LS, etc …”

Section: Mas For Searching the Feature Spacementioning

confidence: 99%

“…The success of an LS algorithm depends on many things: the aptitude of the LS algorithm to exploit the neighborhood, the frequency and the length of the LS, the quality of the individual undergoing an LS, etc. 30 We designed five different LS search algorithms (LS1, LS2, LS3, LS4, and LS5). The general structure of these LS algorithms is shown in Algorithm 2.…”

Section: Local Searchmentioning

confidence: 99%

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An adaptive memetic algorithm for feature selection using proximity graphs

Zaher

Berretta

Noman

et al. 2018

Computational Intelligence

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We propose a multivariate feature selection method that uses proximity graphs for assessing the quality of feature subsets. Initially, a complete graph is built, where nodes are the samples, and edge weights are calculated considering only the selected features. Next, a proximity graph is constructed on the basis of these weights and different fitness functions, calculated over the proximity graph, to evaluate the quality of the selected feature set. We propose an iterative methodology on the basis of a memetic algorithm for exploring the space of possible feature subsets aimed at maximizing a quality score. We designed multiple local search strategies, and we used an adaptive strategy for automatic balancing between the global and local search components of the memetic algorithm. The computational experiments were carried out using four well‐known data sets. We investigate the suitability of three different proximity graphs (minimum spanning tree, k‐nearest neighbors, and relative neighborhood graph) for the proposed approach. The selected features have been evaluated using a total of 49 classification methods from an open‐source data mining and machine learning package (WEKA). The computational results show that the proposed adaptive memetic algorithm can perform better than traditional genetic algorithms in finding more useful feature sets. Finally, we establish the competitiveness of our approach by comparing it with other well‐known feature selection methods.

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Section: Mas For Searching the Feature Spacementioning

confidence: 99%

Section: Local Searchmentioning

confidence: 99%

An adaptive memetic algorithm for feature selection using proximity graphs

Zaher

Berretta

Noman

et al. 2018

Computational Intelligence

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A multi-cycled sequential memetic computing approach for constrained optimisation

Sun

Garibaldi

Zhang

et al. 2016

Information Sciences

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Abdallah (2016) A multicycled sequential memetic computing approach for constrained optimisation. Information Sciences, 340-341 . pp. 175-190. ISSN 1872-6291 Access from the University of Nottingham repository: http://eprints.nottingham.ac.uk/35193/1/ICMA_FinalVersion.pdf Copyright and reuse:The Nottingham ePrints service makes this work by researchers of the University of Nottingham available open access under the following conditions. This article is made available under the Creative Commons Attribution Non-commercial No Derivatives licence and may be reused according to the conditions of the licence. For more details see: http://creativecommons.org/licenses/by-nc-nd/2.5/ A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. AbstractIn this paper, we propose a multi-cycled sequential memetic computing structure for constrained optimisation. The structure is composed of multiple evolutionary cycles.At each cycle, an evolutionary algorithm is considered as an operator, and connects with a local optimiser. This structure enables the learning of useful knowledge from previous cycles and the transfer of the knowledge to facilitate search in latter cycles.Specifically, we propose to apply an estimation of distribution algorithm (EDA) to explore the search space until convergence at each cycle. A local optimiser, called DONLP2, is then applied to improve the best solution found by the EDA. New cycle starts after the local improvement if the computation budget has not been exceeded.In the developed EDA, an adaptive fully-factorized multivariate probability model is proposed. A learning mechanism, implemented as the guided mutation operator, is adopted to learn useful knowledge from previous cycles.The developed algorithm was experimentally studied on the benchmark problems in the CEC 2006 and 2010 competition. Experimental studies have shown that the developed probability model exhibits excellent exploration capability and the learning mechanism can significantly improve the search efficiency under certain conditions.The comparison against some well-known algorithms showed the superiority of the de- * Corresponding author Email address: j.sun@greenwich.ac.uk (Jianyong Sun)Preprint submitted to Information Sciences July 19, 2016 veloped algorithm in terms of the consumed fitness evaluations and the solution quality.

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A memetic algorithm for dynamic economic load dispatch optimization

Orike

Corne

2013

2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE)

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When and what kind of memetic algorithms perform well

Cited by 4 publications

References 6 publications

An adaptive memetic algorithm for feature selection using proximity graphs

An adaptive memetic algorithm for feature selection using proximity graphs

A multi-cycled sequential memetic computing approach for constrained optimisation

A memetic algorithm for dynamic economic load dispatch optimization

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