Please use this identifier to cite or link to this item:
Title: Evolutionary multi-objective optimization using neural-based estimation of distribution algorithms
Keywords: Estimation of distribution algorithm, evolutionary algorithm, hybrid, memetic, multi-objective optimization, restricted Boltzmann machine
Issue Date: 8-Aug-2012
Source: SHIM VUI ANN (2012-08-08). Evolutionary multi-objective optimization using neural-based estimation of distribution algorithms. ScholarBank@NUS Repository.
Abstract: Multi-objective optimization is widely found in many fields, such as logistics, economics, engineering, bioinformatics, finance, or any problems whenever two or more conflicting objectives need to be optimized simultaneously. The synergy of probabilistic graphical approaches in evolutionary computation, commonly known as estimation of distribution algorithms (EDA), may enhance the iterative search process when interrelationships of the archived data has been learnt, modeled, and used in the reproduction. The primary aim of this thesis is to develop a novel neural-based EDA in the context of multi-objective optimization and to implement the algorithm to solve problems with vastly different characteristics and representation schemes. The modeling and sampling issues pertaining to the neural-based EDA will be highlighted. Subsequently, the research directed to study the optimization performance of the neural-based EDA in solving scalable, many-objective, epistatic, noisy, and permutation-based multi-objective optimization problems.
Appears in Collections:Ph.D Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
ShimVA.pdf2.49 MBAdobe PDF



Page view(s)

checked on Dec 2, 2017


checked on Dec 2, 2017

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.