2011 Fiscal Year Final Research Report
Development of Multi-objective Evolutionary Algorithm Based on Local Fitness Function Landscapes for Combinatorial Optimization Problems
| Project/Area Number |
22700158
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Intelligent informatics
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| Research Institution | Kansai University |
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Principal Investigator |
HANADA Yoshiko 関西大学, システム理工学部, 助教 (30511711)
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| Project Period (FY) |
2010 – 2011
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| Keywords | 最適化 / 進化計算 / 多目的最適化 / 組合せ最適化 / 局所探索 |
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| Research Abstract |
In this study, we developed a widely applicable multi-objective genetic algorithm that can find a set of well-distributed solutions which approximates the entire Pareto front. Our proposed method consists of an interpolation-direct multistep crossover and an extrapolation-direct multistep crossover which consider the local ruggedness in objective functions. We evaluated the effectiveness of the method against the levels of intensity of ruggedness in each objective function through the experiments with multi-objective NK model that explains various intrinsic structures observed in combinatorial problems. In addition we applied our method to the design of noise removal filters of digital images.
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