2018 Fiscal Year Annual Research Report
Improving evolutionary algorithms from population structures and interaction networks
| Project/Area Number |
17K12751
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| Research Institution | University of Toyama |
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Principal Investigator |
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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| Project Period (FY) |
2017-04-01 – 2019-03-31
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| Keywords | Intelligent algorithm / Population structure / Evolutionary algorithms / Optimization |
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| Outline of Annual Research Achievements |
Through implementing this research project, the effects of solution population on the performance of algorithms are systematically studied, including the population structure and evolutionary dynamics. The extraction of the generic characteristics of the information interaction network constructed by the population was formulated and studied, and the systematical generation of effective search algorithms from the aspect of population structure was designed and analyzed.
After research, some achievements were realized: 1. We successfully introduced 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 successfully designed for a number of algorithms, such as gravitational search algorithm, particle swarm optimization, multi-objective multiple-valued logic network learning, differential evolution, etc; 4. We have found two rules of population structure when solving the optimization problem (i.e. problem-independent) with single objectives.
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Research Products
(11 results)
