Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142107
Title: Meme-based computational optimization framework
Authors: Dwiyasa, Felis
Lim, Meng-Hiot
Foo, Ren-Xiang
Teo, Jason Shi-Wei
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Dwiyasa, F., Lim, M.-H., Foo, R.-X., Teo, J. S.-W. (2020). Meme-based computational optimization framework. Proceedings of Soft Computing for Problem Solving 2019, 1139, 155-165. doi:10.1007/978-981-15-3287-0_12
Project: C-RP1
Abstract: From a computing perspective, a meme denotes information that represents knowledge, patterns, rules, or strategies used to solve complex problems. When applied on a problem, memes help a solver to arrive at good quality solutions more efficiently, guiding the search process according to certain procedures or rules, instead of randomly searching through the solution space. Depending on the complexity of the problems, evaluating the suitability of memes and selecting a set of effective memes for different problems, however, are not straightforward tasks. A meme that works well for some problems may not be effective for other problems. Besides, different memes might have different degrees of importance in solving a problem. The level of importance of each meme might also change at different stages of the search. In this paper, we discuss how multiple memes can be generated and applied to solve computational optimization problems. A case study in combinatorial optimization is also presented and discussed.
URI: https://hdl.handle.net/10356/142107
ISBN: 978-981-15-3286-3
DOI: 10.1007/978-981-15-3287-0_12
Rights: © 2020 Springer Nature Singapore Pte Ltd. All rights reserved. This paper was published in Proceedings of Soft Computing for Problem Solving 2019 and is made available with permission of Springer Nature Singapore Pte Ltd.
Fulltext Permission: embargo_20210412
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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