2012 Fiscal Year Final Research Report
Effective Population and Training Data Partitioning in Parallel Distributed Evolutionary Knowledge Acquisition
| Project/Area Number |
22700239
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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 |
Sensitivity informatics/Soft computing
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| Research Institution | Osaka Prefecture University |
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Principal Investigator |
NOJIMA Yusuke 大阪府立大学, 大学院・工学研究科, 助教 (10382235)
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| Project Period (FY) |
2010 – 2012
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| Keywords | 遺伝的アルゴリズム |
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| Research Abstract |
Evolutionary knowledge acquisition has beenproposed in order to obtain rule-based knowledge from numerical data. The main problem of this method is that huge computation cost is necessarywhen we apply it to large data. This study proposes parallel distributed implementation of evolutionary knowledge acquisition where both a population and training data are divided into subpopulations and training data subsets, respectively. The computational experiments show that the computational cost can be drastically reduced without the deterioration of the generalization ability. The effects of various specifications for parallel distributed implementation are also examined.
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