會議論文
| 學年 | 97 |
|---|---|
| 學期 | 2 |
| 發表日期 | 2009-05-18 |
| 作品名稱 | A Self-guided Genetic Algorithm for Flowshop Scheduling problems |
| 作品名稱（其他語言） | |
| 著者 | Shih-Shin Chen; Pei-Chann Chang; Qingfu Zhang |
| 作品所屬單位 | |
| 出版者 | |
| 會議名稱 | Proceeding of Congress of Evolutionary Computation 2009 (CEC 2009) |
| 會議地點 | Trondheim, Norway |
| 摘要 | This paper proposed self-guided genetic algorithm, which is one of the algorithms in the category of evolutionary algorithm based on probabilistic models (EAPM), to solve strong NP-hard flowshop scheduling problems with the minimization of makespan. Most EAPM research explicitly used the probabilistic model from the parental distribution, then generated solutions by sampling from the probabilistic model without using genetic operators. Although EAPM is promising in solving different kinds of problems, self-guided GA doesn't intend to generate solution by the probabilistic model directly because the time complexity is high when we solve combinatorial problems, particularly the sequencing ones. As a result, the probabilistic model serves as a fitness surrogate which estimates the fitness of the new solution beforehand in this research. So the probabilistic model is used to guide the evolutionary process of crossover and mutation. This research studied the flowshop scheduling problems and the corresponding experiment were conducted. From the results, it shows that the self-guided GA outperformed other algorithms significantly. In addition, self-guided GA works more efficiently than previous EAPM. As a result, self-guided GA is promising in solving the flowshop scheduling problems. |
| 關鍵字 | Genetic algorithms;Genetic mutations;Evolutionary computation;Sampling methods;Electronic mail;Scheduling algorithm;Minimization methods;Predictive models;Biological cells;Character generation |
| 語言 | en |
| 收錄於 | |
| 會議性質 | 國際 |
| 校內研討會地點 | 無 |
| 研討會時間 | 20090518~20090521 |
| 通訊作者 | |
| 國別 | NOR |
| 公開徵稿 | |
| 出版型式 | |
| 出處 | |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/121538 ) |