当研究室で取り組んでいる研究に関する論文・講演などの情報（一部抜粋）です。
Here is a selection of our published papers on the research we’ve been working on in our laboratory.
Our Selected Papers and Keywords
2011
Kengo Katayama, Akinori Kohmura, Keiko Kohmoto, Hideo Minamihara
Memetic algorithm with strategic controller for the maximum clique problem Proceedings Article
In: SAC 2011: Proceedings of the 2011 ACM Symposium on Applied Computing, pp. 1062-1069, 2011.
Abstract | BibTeX | タグ: Maximum clique problem, Memetic algorithm, Metaheuristics | Links:
@inproceedings{KATAYAMA-SAC-2011,
title = {Memetic algorithm with strategic controller for the maximum clique problem},
author = {Kengo Katayama and Akinori Kohmura and Keiko Kohmoto and Hideo Minamihara},
doi = {https://doi.org/10.1145/1982185.1982419},
year = {2011},
date = {2011-03-21},
urldate = {2011-03-21},
booktitle = {SAC 2011: Proceedings of the 2011 ACM Symposium on Applied Computing},
pages = {1062-1069},
abstract = {Most of standard evolutionary algorithms consist of a mutation, a crossover, a selection and often a local search. Each of these operators is specifically designed for a combinatorial optimization problem. These can be considered as tools for the optimization searches, and the interplay between them in the searches is not apparently controlled in many cases.
In this paper, we present a flexible control method, called Strategic Controller (SC), for multiple mutation methods equipped in a memetic algorithm (MA) for the maximum clique problem (MCP). The SC is used to choose a suitable method from the candidate mutations. To perform an adaptive search, the SC evaluates each mutation method based on the contribution information which is served as novel "memes" for the mutations in the MA. To achieve the SC, we apply the idea of analytic hierarchy process.
Although standard MAs have a population of multiple solutions as memes usually, a single solution is used in our MA. We evaluated the performance of MA with SC (MA-SC) on DIMACS benchmark graphs of the MCP. The results showed that MA-SC is capable of finding comprehensive solutions through comparisons with MAs in which each mutation is used. Moreover, we observed that it is highly effective particularly for hardest graphs in the benchmark set in comparisons with recent metaheuristics to the MCP.},
keywords = {Maximum clique problem, Memetic algorithm, Metaheuristics},
pubstate = {published},
tppubtype = {inproceedings}
}
Most of standard evolutionary algorithms consist of a mutation, a crossover, a selection and often a local search. Each of these operators is specifically designed for a combinatorial optimization problem. These can be considered as tools for the optimization searches, and the interplay between them in the searches is not apparently controlled in many cases.
In this paper, we present a flexible control method, called Strategic Controller (SC), for multiple mutation methods equipped in a memetic algorithm (MA) for the maximum clique problem (MCP). The SC is used to choose a suitable method from the candidate mutations. To perform an adaptive search, the SC evaluates each mutation method based on the contribution information which is served as novel "memes" for the mutations in the MA. To achieve the SC, we apply the idea of analytic hierarchy process.
Although standard MAs have a population of multiple solutions as memes usually, a single solution is used in our MA. We evaluated the performance of MA with SC (MA-SC) on DIMACS benchmark graphs of the MCP. The results showed that MA-SC is capable of finding comprehensive solutions through comparisons with MAs in which each mutation is used. Moreover, we observed that it is highly effective particularly for hardest graphs in the benchmark set in comparisons with recent metaheuristics to the MCP.
In this paper, we present a flexible control method, called Strategic Controller (SC), for multiple mutation methods equipped in a memetic algorithm (MA) for the maximum clique problem (MCP). The SC is used to choose a suitable method from the candidate mutations. To perform an adaptive search, the SC evaluates each mutation method based on the contribution information which is served as novel "memes" for the mutations in the MA. To achieve the SC, we apply the idea of analytic hierarchy process.
Although standard MAs have a population of multiple solutions as memes usually, a single solution is used in our MA. We evaluated the performance of MA with SC (MA-SC) on DIMACS benchmark graphs of the MCP. The results showed that MA-SC is capable of finding comprehensive solutions through comparisons with MAs in which each mutation is used. Moreover, we observed that it is highly effective particularly for hardest graphs in the benchmark set in comparisons with recent metaheuristics to the MCP.