Development of Solution Mining and Deep Search by Feedback to Search
| Project/Area Number |
15K00336
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Soft computing
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| Research Institution | Nagoya University |
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Principal Investigator |
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| Project Period (FY) |
2015-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2017: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2015: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | Deep Search / 可視化 / ユーザの嗜好 / 進化計算 / 多目的最適化 / Reference Line / 追加探索 / フィードバック / ソフトコンピューティング / 解マイニング / 非対応性 |
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| Outline of Final Research Achievements |
In this research, I developed the Deep Search method for a user’s preference direction by using the concept of reference lines. In this method, a user selects the preference area in the visualized space by plotting the acquired solutions, and reference points are generated in the selected area. Reference lines are defined by making connections between the reference points and the original point. In this research, I carried out the experiments that applied the Deep Search method to a real coded multi-objective knapsack problem and studied the effectiveness of this method. The experimental result showed that the solutions having the desired features could be acquired by moving the original point. The experimental result also showed that the Deep Search method also worked well in MaOPs (Many-Objective Optimization Problems), as well as for 2 objectives. The effect of the Deep Search method increased with the number of objectives.
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Report
(4 results)
Research Products
(32 results)
