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{"insert":{"type":"published_papers"},"similar_merge":{"see_also":[{"@id":"https://ci.nii.ac.jp/naid/130004965146/","label":"url"},{"@id":"https://cir.nii.ac.jp/crid/1390282680343138944/","label":"url"},{"@id":"https://web.db.tokushima-u.ac.jp/cgi-bin/edb_browse?EID=303468","label":"url"}],"paper_title":{"en":"On the Stopping Criterion of Adaptive Weighted Aggregation for Multiobjective Continuous Optimization","ja":"多目的連続関数最適化の解法Adaptive Weighted Aggregationの終了条件に関する一考察"},"authors":{"en":[{"name":"濱田 直希"},{"name":"Nagata Yuichi"},{"name":"小林 重信"},{"name":"Ono Isao"}],"ja":[{"name":"濱田 直希"},{"name":"永田 裕一"},{"name":"小林 重信"},{"name":"小野 功"}]},"description":{"en":"This paper proposes a reasonable stopping criterion for Adaptive Weighted Aggregation (AWA), which is a scalarization-based multi-start framework developed in our previous study on continuous multiobjective optimization. Our previous study shows that AWA yields good solutions covering the entire Pareto set and front within a small consumption of running time and function evaluation on 2- to 6-objective benchmark problems. The experimental results also indicate, however, that the number of solutions generated by AWA is multiplied every iteration. The rapid increase of solutions requires a careful choice of the stopping criterion: even one iteration of shortage may deteriorate the coverage of solutions into an unsatisfactory level and one of excess gives rise to a significant waste of computational resources. We therefore discuss the minimum iteration that AWA yields an enough solution set to cover the Pareto set and front in the sense that the set contains at least one interior point of each of their non-empty \"faces\", that is, boundary submanifolds induced from the Pareto sets of subproblems with the same inclusion relation as faces of the simplex. Then, such an iteration, named the representing iteration, is proposed as a stopping criterion for AWA, and the number of solutions found by the representing iteration, named the representing number, is derived to analyze the space complexity of AWA. We also discuss the time complexity of AWA based on numerical experiments. The distribution of obtained solutions and its coverage measure show the usefulness of the proposed stopping criterion.","ja":"This paper proposes a reasonable stopping criterion for Adaptive Weighted Aggregation (AWA), which is a scalarization-based multi-start framework developed in our previous study on continuous multiobjective optimization. Our previous study shows that AWA yields good solutions covering the entire Pareto set and front within a small consumption of running time and function evaluation on 2- to 6-objective benchmark problems. The experimental results also indicate, however, that the number of solutions generated by AWA is multiplied every iteration. The rapid increase of solutions requires a careful choice of the stopping criterion: even one iteration of shortage may deteriorate the coverage of solutions into an unsatisfactory level and one of excess gives rise to a significant waste of computational resources. We therefore discuss the minimum iteration that AWA yields an enough solution set to cover the Pareto set and front in the sense that the set contains at least one interior point of each of their non-empty \"faces\", that is, boundary submanifolds induced from the Pareto sets of subproblems with the same inclusion relation as faces of the simplex. Then, such an iteration, named the representing iteration, is proposed as a stopping criterion for AWA, and the number of solutions found by the representing iteration, named the representing number, is derived to analyze the space complexity of AWA. We also discuss the time complexity of AWA based on numerical experiments. The distribution of obtained solutions and its coverage measure show the usefulness of the proposed stopping criterion."},"publication_date":"2013-03","publication_name":{"en":"Transaction of the Japanese Society for Evolutionary Computation","ja":"進化計算学会論文誌"},"volume":"Vol.4","number":"No.1","starting_page":"13","ending_page":"27","languages":["jpn"],"referee":true,"identifiers":{"doi":["10.11394/tjpnsec.4.13"],"issn":["2185-7385"]},"published_paper_type":"scientific_journal"},"priority":"input_data"}
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{"insert":{"type":"published_papers"},"similar_merge":{"see_also":[{"@id":"https://ci.nii.ac.jp/naid/130004965145/","label":"url"},{"@id":"https://cir.nii.ac.jp/crid/1390001205366429312/","label":"url"},{"@id":"https://web.db.tokushima-u.ac.jp/cgi-bin/edb_browse?EID=303466","label":"url"}],"paper_title":{"en":"Big-valley Explorer: A Framework of Real-coded Genetic Algorithms for Multi-funnel Function Optimization","ja":"大域的多峰性関数最適化のための実数値GAの枠組みBig-valley Explorerの提案"},"authors":{"en":[{"name":"上村 健人"},{"name":"木下 峻一"},{"name":"Nagata Yuichi"},{"name":"小林 重信"},{"name":"Ono Isao"}],"ja":[{"name":"上村 健人"},{"name":"木下 峻一"},{"name":"永田 裕一"},{"name":"小林 重信"},{"name":"小野 功"}]},"description":{"en":"This paper proposes a new framework of real-coded genetic algorithms (RCGAs) for the multi-funnel function optimization. The RCGA is one of the most powerful function optimization methods. Most conventional RCGAs work effectively on the single-funnel function that consists of a single big-valley. However, it is reported that they show poor performance or, sometimes, fail to find the optimum on the multi-funnel function that consists of multiple big-valleys. In order to remedy this deterioration, Innately Split Model (ISM) has been proposed as a framework of RCGAs. ISM initializes an RCGA in a small region and repeats a search with the RCGA as changing the position of the region randomly. ISM outperforms conventional RCGAs on the multi-funnel functions. However, ISM has two problems in terms of the search efficiency and the difficulty of setting parameters. Our proposed method, Big-valley Explorer (BE), is a framework of RCGAs like ISM and it has two novel mechanisms to overcome these problems, the big-valley estimation mechanism and the adaptive initialization mechanism. Once the RCGA finishes a search, the big-valley estimation mechanism estimates a big-valley that the RCGA already explored and removes the region from the search space to prevent the RCGA from searching the same big-valley many times. After that, the adaptive initialization mechanism initializes the RCGA in a wide unexplored region adaptively to find unexplored big-valleys. We evaluate BE through some numerical experiments with both single-funnel and multi-funnel benchmark functions.","ja":"This paper proposes a new framework of real-coded genetic algorithms (RCGAs) for the multi-funnel function optimization. The RCGA is one of the most powerful function optimization methods. Most conventional RCGAs work effectively on the single-funnel function that consists of a single big-valley. However, it is reported that they show poor performance or, sometimes, fail to find the optimum on the multi-funnel function that consists of multiple big-valleys. In order to remedy this deterioration, Innately Split Model (ISM) has been proposed as a framework of RCGAs. ISM initializes an RCGA in a small region and repeats a search with the RCGA as changing the position of the region randomly. ISM outperforms conventional RCGAs on the multi-funnel functions. However, ISM has two problems in terms of the search efficiency and the difficulty of setting parameters. Our proposed method, Big-valley Explorer (BE), is a framework of RCGAs like ISM and it has two novel mechanisms to overcome these problems, the big-valley estimation mechanism and the adaptive initialization mechanism. Once the RCGA finishes a search, the big-valley estimation mechanism estimates a big-valley that the RCGA already explored and removes the region from the search space to prevent the RCGA from searching the same big-valley many times. After that, the adaptive initialization mechanism initializes the RCGA in a wide unexplored region adaptively to find unexplored big-valleys. We evaluate BE through some numerical experiments with both single-funnel and multi-funnel benchmark functions."},"publication_date":"2013-03","publication_name":{"en":"Transaction of the Japanese Society for Evolutionary Computation","ja":"進化計算学会論文誌"},"volume":"Vol.4","number":"No.1","starting_page":"13","ending_page":"27","languages":["jpn"],"referee":true,"identifiers":{"doi":["10.11394/tjpnsec.4.1"],"issn":["2185-7385"]},"published_paper_type":"scientific_journal"},"priority":"input_data"}
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{"insert":{"type":"published_papers"},"similar_merge":{"see_also":[{"@id":"http://ci.nii.ac.jp/naid/130000259120/","label":"url"},{"@id":"https://cir.nii.ac.jp/crid/1390001205109072384/","label":"url"},{"@id":"https://www.scopus.com/record/display.url?eid=2-s2.0-77955938081&origin=inward","label":"url"},{"@id":"https://web.db.tokushima-u.ac.jp/cgi-bin/edb_browse?EID=303455","label":"url"}],"paper_title":{"en":"Analysis of The Behavior of MGG and JGG As A Selection Model for Real-coded Genetic Algorithms","ja":"実数値GAにおける生存選択モデルとしてのMGGとJGGの挙動解析"},"authors":{"en":[{"name":"秋本 洋平"},{"name":"Nagata Yuichi"},{"name":"佐久間 淳"},{"name":"小野 功"},{"name":"小林 重信"}],"ja":[{"name":"秋本 洋平"},{"name":"永田 裕一"},{"name":"佐久間 淳"},{"name":"小野 功"},{"name":"小林 重信"}]},"description":{"en":"In this paper, we focus on analyzing the behavior of the selection models for real-coded genetic algorithms. Recent studies show that Just Generation Gap (JGG) selection model outperforms Minimal Generation Gap (MGG) model when a multi-parental crossover operator based on the hypothesis of the preservation of the statistics of parents is used. However, the validation of JGG selection model is not done yet. To validate the selection method of JGG, we analyze the differences of the behavior of JGG selection model and that of MGG selection model.","ja":"In this paper, we focus on analyzing the behavior of the selection models for real-coded genetic algorithms. Recent studies show that Just Generation Gap (JGG) selection model outperforms Minimal Generation Gap (MGG) model when a multi-parental crossover operator based on the hypothesis of the preservation of the statistics of parents is used. However, the validation of JGG selection model is not done yet. To validate the selection method of JGG, we analyze the differences of the behavior of JGG selection model and that of MGG selection model."},"publication_date":"2010-03","publication_name":{"en":"Transactions of the Japanese Society for Artificial Intelligence","ja":"人工知能学会論文誌"},"volume":"Vol.25","number":"No.2","starting_page":"281","ending_page":"289","languages":["jpn"],"referee":true,"identifiers":{"doi":["10.1527/tjsai.25.281"],"issn":["1346-0714"]},"published_paper_type":"scientific_journal"},"priority":"input_data"}
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{"insert":{"type":"published_papers"},"similar_merge":{"see_also":[{"@id":"http://ci.nii.ac.jp/naid/130000137880/","label":"url"},{"@id":"https://cir.nii.ac.jp/crid/1390001205108974592/","label":"url"},{"@id":"https://www.scopus.com/record/display.url?eid=2-s2.0-70350128255&origin=inward","label":"url"},{"@id":"https://web.db.tokushima-u.ac.jp/cgi-bin/edb_browse?EID=303453","label":"url"}],"paper_title":{"en":"Proposal and Evaluation of Adaptive Real-coded Crossover AREX","ja":"適応的実数値交叉 AREX の提案と評価"},"authors":{"en":[{"name":"秋本 洋平"},{"name":"Nagata Yuichi"},{"name":"佐久間 淳"},{"name":"小野 功"},{"name":"小林 重信"}],"ja":[{"name":"秋本 洋平"},{"name":"永田 裕一"},{"name":"佐久間 淳"},{"name":"小野 功"},{"name":"小林 重信"}]},"description":{"en":"Since once premature convergence happens evolutionary algorithms for function optimization can no longer explore areas of the search space and fail to find the optimum, it is required to handle the notorious drawback. This paper proposes two novel approaches to overcome premature convergence of real-coded genetic algorithms (RCGAs). The first idea is to control the sampling region of crossover by adaptation of expansion rate. The second idea is to cause the acceleration of the movement of population by descending the mean of crossover. Finally, we propose a crossover that combines the adaptation of expansion rate technique and the crossover mean descent technique, called AREX (adaptive real-coded ensemble crossover). The performance of the real-coded GA using AREX is evaluated on several benchmark functions including functions whose landscape forms ridge structure or multi-peak structure, both of which are likely to lead to the miserable convergence phenomenon. The experimental results show not only that the proposed method can locate the global optima of functions on which it is difficult for the existing GAs to discover it but also that our approach outperforms the existing one in number of function evaluations on all functions. Our approach enlarges the classes of functions that real-coded GAs can solve.","ja":"Since once premature convergence happens evolutionary algorithms for function optimization can no longer explore areas of the search space and fail to find the optimum, it is required to handle the notorious drawback. This paper proposes two novel approaches to overcome premature convergence of real-coded genetic algorithms (RCGAs). The first idea is to control the sampling region of crossover by adaptation of expansion rate. The second idea is to cause the acceleration of the movement of population by descending the mean of crossover. Finally, we propose a crossover that combines the adaptation of expansion rate technique and the crossover mean descent technique, called AREX (adaptive real-coded ensemble crossover). The performance of the real-coded GA using AREX is evaluated on several benchmark functions including functions whose landscape forms ridge structure or multi-peak structure, both of which are likely to lead to the miserable convergence phenomenon. The experimental results show not only that the proposed method can locate the global optima of functions on which it is difficult for the existing GAs to discover it but also that our approach outperforms the existing one in number of function evaluations on all functions. Our approach enlarges the classes of functions that real-coded GAs can solve."},"publication_date":"2009-11","publication_name":{"en":"Transactions of the Japanese Society for Artificial Intelligence","ja":"人工知能学会論文誌"},"volume":"Vol.24","number":"No.6","starting_page":"446","ending_page":"458","languages":["jpn"],"referee":true,"identifiers":{"doi":["10.1527/tjsai.24.446"],"issn":["1346-0714"]},"published_paper_type":"scientific_journal"},"priority":"input_data"}
{"insert":{"type":"published_papers"},"similar_merge":{"see_also":[{"@id":"https://www.scopus.com/record/display.url?eid=2-s2.0-69549111047&origin=inward","label":"url"},{"@id":"https://web.db.tokushima-u.ac.jp/cgi-bin/edb_browse?EID=303451","label":"url"}],"paper_title":{"en":"A Powerful Route Minimization heuristic for the Vehicle Routing Problem with Time Windows","ja":"A Powerful Route Minimization heuristic for the Vehicle Routing Problem with Time Windows"},"authors":{"en":[{"name":"Nagata Yuichi"},{"name":"Bräysy Olli"}],"ja":[{"name":"永田 裕一"},{"name":"Bräysy Olli"}]},"publication_date":"2009-09","publication_name":{"en":"Operations Research Letters","ja":"Operations Research Letters"},"volume":"Vol.37","number":"No.5","starting_page":"333","ending_page":"338","languages":["eng"],"referee":true,"identifiers":{"doi":["10.1016/j.orl.2009.04.006"],"issn":["0167-6377"]},"published_paper_type":"scientific_journal"},"priority":"input_data"}
{"insert":{"type":"published_papers"},"similar_merge":{"see_also":[{"@id":"http://ci.nii.ac.jp/naid/130000098273/","label":"url"},{"@id":"https://cir.nii.ac.jp/crid/1390282680083022592/","label":"url"},{"@id":"https://www.scopus.com/record/display.url?eid=2-s2.0-59349089864&origin=inward","label":"url"},{"@id":"https://web.db.tokushima-u.ac.jp/cgi-bin/edb_browse?EID=303450","label":"url"}],"paper_title":{"en":"An Adaptive Niching Genetic Algorithm using a niche size equalization mechanism","ja":"均等に個体を分散化する適応的ニッチングGAの提案"},"authors":{"en":[{"name":"Nagata Yuichi"}],"ja":[{"name":"永田 裕一"}]},"description":{"en":"Niching GAs have been widely investigated to apply genetic algorithms (GAs) to multimodal function optimization problems. In this paper, we suggest a new niching GA that attempts to form niches, each consisting of an equal number of individuals. The proposed GA can be applied also to combinatorial optimization problems by defining a distance metric in the search space. We apply the proposed GA to the job-shop scheduling problem (JSP) and demonstrate that the proposed niching method enhances the ability to maintain niches and improve the performance of GAs.","ja":"Niching GAs have been widely investigated to apply genetic algorithms (GAs) to multimodal function optimization problems. In this paper, we suggest a new niching GA that attempts to form niches, each consisting of an equal number of individuals. The proposed GA can be applied also to combinatorial optimization problems by defining a distance metric in the search space. We apply the proposed GA to the job-shop scheduling problem (JSP) and demonstrate that the proposed niching method enhances the ability to maintain niches and improve the performance of GAs."},"publication_date":"2009-01","publication_name":{"en":"Transactions of the Japanese Society for Artificial Intelligence","ja":"人工知能学会論文誌"},"volume":"Vol.24","number":"No.1","starting_page":"92","ending_page":"103","languages":["jpn"],"referee":true,"identifiers":{"doi":["10.1527/tjsai.24.92"],"issn":["1346-0714"]},"published_paper_type":"scientific_journal"},"priority":"input_data"}
{"insert":{"type":"published_papers"},"similar_merge":{"see_also":[{"@id":"http://ci.nii.ac.jp/naid/10022008102/","label":"url"},{"@id":"https://cir.nii.ac.jp/crid/1390001205108326656/","label":"url"},{"@id":"https://web.db.tokushima-u.ac.jp/cgi-bin/edb_browse?EID=303448","label":"url"}],"paper_title":{"en":"Fast Implementation of Genetic Algorithm by Localized EAX Crossover for the Traveling Salesman Problem","ja":"局所的な交叉EAXを用いたGAの高速化とTSPへの適用"},"authors":{"en":[{"name":"Nagata Yuichi"}],"ja":[{"name":"永田 裕一"}]},"description":{"en":"We propose an genetic algorithm (GA) that applies to the traveling salesman problem (TSP). The GA uses edge assembly crossover (EAX), which is known to be effective for solving the TSP. We first propose a fast implementation of a localized EAX where localized edge exchanges are used in the EAX procedure. We also propose a selection model with an effective combination of the localized EAX that can maintain population diversity at negligible computational costs. Edge entropy measure is used to evaluate population diversity. We demonstrate that the proposed GA is comparable to state-of-the-art heuristics for the TSP. Especially, the GA is superior to them on large instances more than 10,000 cities. For example, the GA found an optimal solution of brd14051 (14,051 cities instance) with a reasonable computational cost. The results are quite impressive because the GA does not use Lin-Kernighan local search (LKLS) even though almost all existing state-of-the-art heuristics for the TSP based on LKLS and its variants.","ja":"We propose an genetic algorithm (GA) that applies to the traveling salesman problem (TSP). The GA uses edge assembly crossover (EAX), which is known to be effective for solving the TSP. We first propose a fast implementation of a localized EAX where localized edge exchanges are used in the EAX procedure. We also propose a selection model with an effective combination of the localized EAX that can maintain population diversity at negligible computational costs. Edge entropy measure is used to evaluate population diversity. We demonstrate that the proposed GA is comparable to state-of-the-art heuristics for the TSP. Especially, the GA is superior to them on large instances more than 10,000 cities. For example, the GA found an optimal solution of brd14051 (14,051 cities instance) with a reasonable computational cost. The results are quite impressive because the GA does not use Lin-Kernighan local search (LKLS) even though almost all existing state-of-the-art heuristics for the TSP based on LKLS and its variants."},"publication_date":"2007-09","publication_name":{"en":"Transactions of the Japanese Society for Artificial Intelligence","ja":"人工知能学会論文誌"},"volume":"Vol.22","number":"No.5","starting_page":"524","ending_page":"552","languages":["jpn"],"referee":true,"identifiers":{"doi":["10.1527/tjsai.22.542"],"issn":["1346-0714"]},"published_paper_type":"scientific_journal"},"priority":"input_data"}
{"insert":{"type":"published_papers"},"similar_merge":{"see_also":[{"@id":"http://ci.nii.ac.jp/naid/10022006211/","label":"url"},{"@id":"https://cir.nii.ac.jp/crid/1390282680083497216/","label":"url"},{"@id":"https://www.scopus.com/record/display.url?eid=2-s2.0-32044441664&origin=inward","label":"url"},{"@id":"https://web.db.tokushima-u.ac.jp/cgi-bin/edb_browse?EID=288332","label":"url"}],"paper_title":{"en":"New Approach of a Genetic Algorithm for TSP Using the Evaluation Function Considering Local Diversity Loss","ja":"局所的多様性の損失を考慮した評価関数を用いたGAのTSPへの適用,"},"authors":{"en":[{"name":"Nagata Yuichi"}],"ja":[{"name":"永田 裕一"}]},"description":{"en":"The edge assembly crossover (EAX) is considered the best available crossover for traveling salesman problems (TSPs). In this paper, a modified EAX algorithm is proposed. The key idea is to maintain population diversity by eliminating any exchanges of edges by the crossover that does not contribute to an improved evaluation value. For this, a new evaluation function is designed considering local diversity loss of the population. The proposed method is applied to several benchmark instances with up to 4461 cities. Experimental results show that the proposed method works better than other genetic algorithms using other improvements of the EAX. The proposed method can reach optimal solutions for most benchmark instances with up to 2392 cities with probabilities higher than 90%. For an instance called fnl4461, this method can reach an optimal solution with probability 60% when the population size is set to 300 -- an extremely small population compared to that needed in previous studies.","ja":"The edge assembly crossover (EAX) is considered the best available crossover for traveling salesman problems (TSPs). In this paper, a modified EAX algorithm is proposed. The key idea is to maintain population diversity by eliminating any exchanges of edges by the crossover that does not contribute to an improved evaluation value. For this, a new evaluation function is designed considering local diversity loss of the population. The proposed method is applied to several benchmark instances with up to 4461 cities. Experimental results show that the proposed method works better than other genetic algorithms using other improvements of the EAX. The proposed method can reach optimal solutions for most benchmark instances with up to 2392 cities with probabilities higher than 90%. For an instance called fnl4461, this method can reach an optimal solution with probability 60% when the population size is set to 300 -- an extremely small population compared to that needed in previous studies."},"publication_date":"2006-02","publication_name":{"en":"Transactions of the Japanese Society for Artificial Intelligence","ja":"人工知能学会論文誌"},"volume":"Vol.21","number":"No.2","starting_page":"195","ending_page":"204","languages":["jpn"],"referee":true,"identifiers":{"doi":["10.1527/tjsai.21.195"],"issn":["1346-0714"]},"published_paper_type":"scientific_journal"},"priority":"input_data"}
{"insert":{"type":"published_papers"},"similar_merge":{"see_also":[{"@id":"https://web.db.tokushima-u.ac.jp/cgi-bin/edb_browse?EID=288331","label":"url"}],"paper_title":{"en":"巡回セールスマン問題に対する交叉:枝組み立て交叉の提案と評価","ja":"巡回セールスマン問題に対する交叉:枝組み立て交叉の提案と評価"},"authors":{"en":[{"name":"Nagata Yuichi"},{"name":"小林 重信"}],"ja":[{"name":"永田 裕一"},{"name":"小林 重信"}]},"publication_date":"1999-09","publication_name":{"en":"Journal of Japanese Society for Artificial Intelligence","ja":"人工知能学会誌(-2013)"},"volume":"Vol.14","number":"No.5","starting_page":"848","ending_page":"859","languages":["jpn"],"referee":true,"identifiers":{"issn":["0912-8085"]},"published_paper_type":"scientific_journal"},"priority":"input_data"}
{"insert":{"type":"published_papers"},"similar_merge":{"see_also":[{"@id":"https://aiit.ac.jp/documents/jp/research_collab/research/bulletin/17th/014_yang.pdf","label":"url"},{"@id":"https://web.db.tokushima-u.ac.jp/cgi-bin/edb_browse?EID=405804","label":"url"}],"paper_title":{"en":"A classification and improvement method of metaheuristic algorithms based on complex networks","ja":"A classification and improvement method of metaheuristic algorithms based on complex networks"},"authors":{"en":[{"name":"Yifei Yang"},{"name":"Chaofeng Zhang"},{"name":"Wenbin Wang"},{"name":"YANG Haichuan"},{"name":"Nagata Yuichi"}],"ja":[{"name":"Yifei Yang"},{"name":"Chaofeng Zhang"},{"name":"Wenbin Wang"},{"name":"楊 海川"},{"name":"永田 裕一"}]},"publication_date":"2024-02","publication_name":{"en":"Bulletin of Advanced Institute of Industrial Technology","ja":"Bulletin of Advanced Institute of Industrial Technology"},"number":"No.17","starting_page":"94","ending_page":"99","languages":["eng"],"published_paper_type":"research_institution"},"priority":"input_data"}