研究課題/領域番号 |
17K12751
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研究機関 | 富山大学 |
研究代表者 |
高 尚策 富山大学, 大学院理工学研究部(工学), 准教授 (60734572)
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研究期間 (年度) |
2017-04-01 – 2019-03-31
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キーワード | Intelligent algorithm / Population structure / Complex network / Differential evolution / Optimization |
研究実績の概要 |
In our research, we concentrate our attention on the population which is the common component in all EAs. The population structure and evolutionary dynamics were systematically investigated. The extraction of the generic characteristics of the information interaction network constructed by the population was studied, and the systematical generation of effective search algorithms from the aspect of population structure was designed. 1. We used node degrees to characterize the population interaction network from the view of complex network; 2. We studied population structures affect the information flux in the interaction network; 3. The mechanisms and results of population structures which affect the search performance in terms of solution precision, convergence and population diversity were studied; 4. We have found two rules of population structure which is the most robust (i.e. problem-independent) when solving the optimization problem with single objectives.
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現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
As a result of this research, we have published 10 journal papers and 5 conference papers. Especially, we observed that a power law distribution exists in brain storm optimization (BSO) algorithm and a Poisson law can be derived from population interaction network in differential evolution algorithm: 1. We proposed a population interaction network (PIN) to investigate the relationship constituted by populations. The experimental results demonstrate the CDF meets cumulative Poisson distribution. 2. PIN was used to construct the relationship among individuals in BSO. The experimental results indicated the frequency of average degree of BSO meets a power law distribution in the functions with low dimension, which shows the best performance of algorithm among three kinds of dimensions.
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今後の研究の推進方策 |
In the following research year, we plan to use the population interaction network (PIN) to establish the relationship between other EAs and complex network (CN). Through population dynamic analysis and statistical confidence test via PIN, the topological structure properties (such as degree distribution) in CN can be used to study the issues (population diversity, etc.) in EAs. Theoretical analysis and application verification are also carried out. Furthermore, two key scientific factors will be studied from three aspects: statistics, structures and abstraction.
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次年度使用額が生じた理由 |
The reason of the incurring amount to be used next fiscal year is mainly because the payments of some accepted or conditional accepted papers have not been finished. And we plan to use it as additional fees of personnel expenditure and remuneration.
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