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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yao-Chen Chuang Chyi-Tsong Chen |
| Copyright Year | 2011 |
| Description | Author affiliation: Department of Chemical Engineering, Feng Chia University, Taichung, Taiwan, R.O.C. (Yao-Chen Chuang; Chyi-Tsong Chen) |
| Abstract | In this paper, a novel and efficient real-coded genetic algorithm (RCGA) for process optimization is developed. The proposed RCGA is equipped with Ranking Selection (RS), Direction-Based Crossover (DBX) and Dynamic Random Mutation (DRM) operators. The RS operator is used to eliminate the bad solutions and reproduce good solutions, making the whole population to achieve a better average fitness. The DBX operator uses relative fitness information to direct the crossover toward a direction that significantly improves the objective fitness. The DRM operator prevents the premature convergence of RCGA and at the same time increases the precision of the searched solution. The effectiveness and application of the proposed RCGA are demonstrated through a variety of single-objective optimization benchmark problems. For comparative study, other existing RCGAs with different evolution operators are also performed to the same problem set. Extensive experiment results reveal that the proposed RCGA provides a significantly faster convergence speed and much better search performance than comparative methods. |
| Starting Page | 2488 |
| Ending Page | 2495 |
| File Size | 715722 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424478347 |
| e-ISBN | 9781424478354 |
| DOI | 10.1109/CEC.2011.5949926 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-06-05 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Biological cells Optimization Convergence Genetic algorithms Evolution (biology) Generators Heuristic algorithms process optimization real-coded genetic algorithm direction-based crossover dynamic mutation constrained optimization |
| Content Type | Text |
| Resource Type | Article |
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