Interval type-2 fuzzy inference system for tuning adaptive weighted multi-objective genetic algorithm
| dc.contributor.author | Pong, Kuan Peng | |
| dc.date.accessioned | 2016-11-19T03:30:00Z | |
| dc.date.available | 2016-11-19T03:30:00Z | |
| dc.date.issued | 2013-10 | |
| dc.description.abstract | Many real world optimization problems involve multi-objectives. Multi-objective problems are problem with two or more objectives and generally conflicting with each other. Multi-objective optimization algorithms goals are converge to the set of Pareto optimal solutions and maintain of diversity among Pareto optimal solutions. Multi-objective optimization approaches can be divided into classical approaches and evolutionary algorithms. Classical approaches generally convert multi-objective function into single objective function and involve decision makers in the search. Evolutionary optimization algorithms use a population based approach in which a set of solutions evolves new solutions in the next generation. The use of population of solutions helps to simultaneously find a set of Pareto optimal solution, thus making evolutionary optimization computationally efficient. Genetic algorithm parameter is the key factor to determine genetic algorithm performance. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/4716 | |
| dc.language.iso | en | en_US |
| dc.publisher | Terengganu: Universiti Malaysia Terengganu | en_US |
| dc.subject | QA 402.5 .P6 2013 | en_US |
| dc.subject | Pong, Kuan Peng | en_US |
| dc.subject | Tesis PPIMG 2013 | en_US |
| dc.subject | Mathematical optimization | en_US |
| dc.title | Interval type-2 fuzzy inference system for tuning adaptive weighted multi-objective genetic algorithm | en_US |
| dc.type | Thesis | en_US |
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