The Development And Application Of Evolutionary Computation-Based Layered Encoding Cascade Optimization Model

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dc.contributor.authorNeoh, Siew Chin
dc.date.accessioned2018-08-02T04:25:22Z
dc.date.available2018-08-02T04:25:22Z
dc.date.issued2010-01
dc.description.abstractIn this thesis, the research on a generic evolutionary-based layered encoding cascade optimization (LECO) model that is able to solve different kinds of optimization problems on multi-decision, multi-resolution, interactive, hybridized and multi-objective is presented. In the proposed model, particular attention is given to genetic algorithm (GA) and particle swarm optimization (PSO) in the development of evolutionary-based search mechanism. The foundation of the proposed model is the development of layered encoding representation structure that integrates the ideas of divide and conquer strategy, schema theorem, and hierarchical concepts of semantic network and frame-based representation. Then, based on the insightful mechanisms of the multi-population evolution, cascade correlation, architecture, and strategy, as well as optimization methods, the LECO model is developed. The architecture of the LECO endorses hybridization of different optimization techniques. Different combinations of GA and PSO in LECO are studied to investigate the most appropriate combination of evolutionary mechanism for LECO. A series of empirical studies comprising benchmark and real-world problems is employed to assess the capability, flexibility, and effectiveness of LECO to handle different kinds of optimization problems as well as to be integrated with other heuristic techniques. Besides the datasets given in benchmark and real-world problems, hypothetical data is also included to investigate the performance of LECO towards larger scale problems. The experimental results demonstrate that GA-PSO LECO is able to optimize combinatorial multi-decision scheduling problem, multi-resolution parameter optimization as well as multi-objective Pareto optimization. In addition, the LECO structure that allows particular layer to be easily analyzed and evaluated promotes interactive optimization whereby human xix intervention can be applied on layers for feature extraction. In all, the cascade layered encoding structure that is able to show inheritance of information, separating representation into layers, enhancing balance global-local search, and narrowing down the search space makes the LECO model a flexible, generic, and powerful tool for optimization problems.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/6134
dc.language.isoenen_US
dc.publisherUniversiti Sains Malaysiaen_US
dc.subjectThe Development And Application Of Evolutionary Computation-Baseden_US
dc.subjectLayered Encoding Cascade Optimization Modelen_US
dc.titleThe Development And Application Of Evolutionary Computation-Based Layered Encoding Cascade Optimization Modelen_US
dc.typeThesisen_US
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