| Project/Area Number |
08650465
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
System engineering
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| Research Institution | Kobe University |
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Principal Investigator |
HANEDA Hiromasa Dept.of Electrical and Electronic Engineering, Kobe University, Professor, 工学部, 教授 (10031113)
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| Co-Investigator(Kenkyū-buntansha) |
TAGAWA Kiyoharu Dept.of Electrical and Electronic Engineering, Kobe University, Research Associa, 工学部, 助手 (50252789)
INOUE Katsumi Dept.of Electrical and Electronic Engineering, Kobe University, Associate Profes, 工学部, 助教授 (10252321)
OHTA Yuzo Dept.of Computer and Systems Engineering, Kobe University, Professor, 工学部, 教授 (80111772)
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| Project Period (FY) |
1996 – 1997
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| Project Status |
Completed (Fiscal Year 1997)
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| Budget Amount *help |
¥2,300,000 (Direct Cost: ¥2,300,000)
Fiscal Year 1997: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 1996: ¥1,800,000 (Direct Cost: ¥1,800,000)
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| Keywords | Genetic Algorithm / Metric Function / Crossover Operation / Robot Control / Parallel Processing / Scheduling Problem / 遺伝的アルゴリム / エッジ交叉 |
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| Research Abstract |
We studied practical methodology to design an effective genetic algoritym for the scheduling problem of robot control computation. Then, we proposed the following techniques applicable to the implementation of genetic algorithms for many combinatorial optimization problems.
(1) Encoding of isomorphic genotype : A phenotype is usually represented by several genotypes. We have proposed a new technique to represent each phenotype uniquely by using a set of isomorphic genotypes. We could maintain the diversity of population with the encoding technique.
(2) Phenotypic Distance : We have defined the phenotypic distance between two phenotypes by the least Hamming distance between isomorphic genotypes. By using the phenotypic distance in genetic operations, we could evaluate and control the diversity of population.
(3) Weight-Edge Crossover : We have proposed a new crossover which combines the conventional edge crossover with a heuristic insight to preserve the excellent characteristic of parents. Experimental results showed that the proposed crossover was superior to the conventional ones.
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