Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/158481
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dc.contributor.authorQian, Yanfeien_US
dc.date.accessioned2022-05-26T00:20:15Z-
dc.date.available2022-05-26T00:20:15Z-
dc.date.issued2022-
dc.identifier.citationQian, Y. (2022). Differential evolution with large initial populations. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158481en_US
dc.identifier.urihttps://hdl.handle.net/10356/158481-
dc.description.abstractThis paper proposed a novel method to determine which individuals can enter from the first search phase to the second phase search. An orthogonal array constructs the initial population. The first search phase is neighborhood-based search, and game theory is also introduced. After finishing the first phase, there are two criteria to enter the next phase. One is a traditional standard, fitness. Another is the score, which is generated from the game. This new algorithm, named OGLSHADE-CS, involves other techniques: linear population reduction, success history base adaption, multi-strategy mutation, and conservative selection. This algorithm and some state-of-the-art algorithms test the 2020 CEC benchmark suite. They are compared using some statistic tests. The results show that game theory can improve performance.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Computer science and engineering::Theory of computation::Analysis of algorithms and problem complexityen_US
dc.subjectEngineering::Electrical and electronic engineering::Computer hardware, software and systemsen_US
dc.titleDifferential evolution with large initial populationsen_US
dc.typeThesis-Master by Courseworken_US
dc.contributor.supervisorPonnuthurai Nagaratnam Suganthanen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Computer Control and Automation)en_US
dc.contributor.supervisoremailEPNSugan@ntu.edu.sgen_US
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