Tokushima University / Educator and Researcher Directory --- Nagata, Yuichi

Field of Study

Subject of Study

Book / Paper

Academic Paper (Judged Full Paper):

1.	Yuichi Nagata and Shinji Imahori : Creation of Dihedral Escher-like Tilings Based on As-Rigid-As-Possible Deformation, ACM Transactions on Graphics, Vol.43, No.2:18, 1-18, 2024. (Link to Publication Site) ● Publication site (DOI): 10.1145/3638048 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1145/3638048 (DOI: 10.1145/3638048)
2.	Yuichi Nagata and Shinji Imahori : Escherization with Large Deformations Based on As-Rigid-As-Possible Shape Modeling, ACM Transactions on Graphics, Vol.41, No.2:11, 1-16, 2022. (Link to Publication Site) ● Publication site (DOI): 10.1145/3487017 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1145/3487017 (DOI: 10.1145/3487017)
3.	Yuichi Nagata : High-Order Entropy-based Population Diversity Measures in the Traveling Salesman Problem, Evolutionary Computation, Vol.28, No.4, 595-619, 2020. (Link to Publication Site) ● Publication site (DOI): 10.1162/evco_a_00268 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1162/evco_a_00268 (DOI: 10.1162/evco_a_00268)
4.	Yuichi Nagata and Shinji Imahori : An Efficient Exhaustive Search Algorithm for the Escherization Problem, Algorithmica, Vol.82, No.9, 2502-2534, 2020. (Link to Publication Site) ● Publication site (DOI): 10.1007/s00453-020-00695-6 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85083370991 (DOI: 10.1007/s00453-020-00695-6, Elsevier: Scopus)
5.	Yuichi Nagata, Akinori Imamiya and Norihiko Ono : A genetic algorithm for the picture maze generation problem, Computers & Operations research, Computers & Operations Research, Vol.115, 313-323, 2020. (Tokushima University Institutional Repository) ● Metadata: 114970 (Link to Publication Site) ● Publication site (DOI): 10.1016/j.cor.2019.104860 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1016/j.cor.2019.104860 (Tokushima University Institutional Repository: 114970, DOI: 10.1016/j.cor.2019.104860)
6.	Nobuaki Oki, Norihiko Ono and Yuichi Nagata : Modeling of a Vehicle Routing Problem with Actual Constraints and Re-scheduling for Dynamic Requests, Transactions of the Society of Instrument and Control Engineers, Vol.55, No.4, 313-323, 2019. (Link to Publication Site) ● Publication site (DOI): 10.9746/sicetr.55.313 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390001288129671936 ● Search Scopus @ Elsevier (DOI): 10.9746/sicetr.55.313 (DOI: 10.9746/sicetr.55.313, CiNii: 1390001288129671936)
7.	Keiichiro Terao, Norihiko Ono and Yuichi Nagata : Parallelization of GA-EAX using Identical Population in all Processes, Transaction of the Japanese Society for Evolutionary Computation, Vol.8, No.3, 100-110, 2018. (Summary) One of the most powerful approximation solution methods for the traveling salesman problem (TSP) is a genetic algorithm using edge assembly crossover (GA-EAX), which has found best-known tours to several 100 thousand points scale TSP instances. However, due to the nature of multi-point search, in many cases GAs take more computation time than local search-based algorithms, and it is difficult to fully exercise the capability of GA-EAX for very large TSP instances having more than 1 million points within a reasonable computation time. In this research, we introduce a MPI parallel implementation of GA-EAX. However, in a naive master slave method, the communication costs between the processes are too high to obtain the effect of parallelization sufficiently. So, we introduce a method to reduce the amount of communication between processes to avoid this problem. We also introduce a MPI/thread hybrid parallel implementation of GA-EAX where each MPI process is executed using multiple threads. Experimental results show that the hybrid parallel model achieved up to 29.4 times speedup using 16 PCs, each with 4 cores. (Keyword) traveling salesman problem / genetic algorithm / EAX / parallel computing (Link to Publication Site) ● Publication site (DOI): 10.11394/tjpnsec.8.100 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390001205364382848 ● Search Scopus @ Elsevier (DOI): 10.11394/tjpnsec.8.100 (DOI: 10.11394/tjpnsec.8.100, CiNii: 1390001205364382848)
8.	Takuya Yamabuki, Norihiko Ono and Yuichi Nagata : 同卓スケジューリング問題のモデル化とその動的スケジューリング, Transactions of the Society of Instrument and Control Engineers, Vol.54, No.3, 346-356, 2018. (Link to Publication Site) ● Publication site (DOI): 10.9746/sicetr.54.346 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390282679486408320 ● Search Scopus @ Elsevier (DOI): 10.9746/sicetr.54.346 (DOI: 10.9746/sicetr.54.346, CiNii: 1390282679486408320)
9.	Yuichi Nagata and Isao Ono : A Guided Local Search with Iterative Ejections of Bottleneck Operations for the Job Shop Scheduling Problem, Computers & Operations Research, Vol.90, 60-71, 2018. (Link to Publication Site) ● Publication site (DOI): 10.1016/j.cor.2017.09.017 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85029630823 (DOI: 10.1016/j.cor.2017.09.017, Elsevier: Scopus)
10.	Yuichi Nagata : Random Partial Neighborhood Search for the Post-Enrollment Course Timetabling Problem, Computers & Operations Research, Vol.90, 84-96, 2018. (Link to Publication Site) ● Publication site (DOI): 10.1016/j.cor.2017.09.014 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85029677495 (DOI: 10.1016/j.cor.2017.09.014, Elsevier: Scopus)
11.	山越幸太, Yuichi Nagata and 小野功 : TSPのためのGA-EAXにおける探索ステージ切換条件とマルチスタート戦略の提案, Transactions of the Society of Instrument and Control Engineers, Vol.52, No.4, 242-248, 2016. (Link to Publication Site) ● Publication site (DOI): 10.9746/sicetr.52.242 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390282679485961088 ● Search Scopus @ Elsevier (DOI): 10.9746/sicetr.52.242 (DOI: 10.9746/sicetr.52.242, CiNii: 1390282679485961088)
12.	益富和之, Yuichi Nagata and 小野功 : A Novel Evolution Strategy for Noisy Function Optimization, Transaction of the Japanese Society for Evolutionary Computation, Vol.6, No.1, 1-12, 2015. (Summary) This paper proposes a novel evolution strategy for noisy function optimization. We consider minimization of the expectation of a continuous domain function with stochastic parameters. The proposed method is an extended variant of distance-weighted exponential evolution strategy (DX-NES), which is a state-of-the-art algorithm for deterministic function optimization. We name it DX-NES for uncertain environments (DX-NES-UE). DX-NES-UE estimates the objective function by a quadratic surrogate function. In order to make a balance between speed and accuracy, DX-NES-UE uses surrogate function values when the noise is strong; otherwise it uses observed objective function values. We conduct numerical experiments on 20-dimensional benchmark problems to compare the performance of DX-NES-UE and that of uncertainty handling covariance matrix adaptation evolution strategy (UH-CMA-ES). UH-CMA-ES is one of the most promising methods for noisy function optimization. Benchmark problems include a multimodal function, ill-scaled functions and a non-C<SUP>2</SUP> function with additive noise and decision variable perturbation (sometime called actuator noise). The experiments show that DX-NES-UE requires about 1/100 times as many observations as UH-CMA-ES does on well-scaled functions. The performance difference is greater on ill-scaled functions. (Keyword) DX-NES / UH-CMA-ES / noisy function optimization / surrogate function / stochastic descent / additive noise / decision variable perturbation (Link to Publication Site) ● Publication site (DOI): 10.11394/tjpnsec.6.1 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390282680341421184 ● Search Scopus @ Elsevier (DOI): 10.11394/tjpnsec.6.1 (DOI: 10.11394/tjpnsec.6.1, CiNii: 1390282680341421184)
13.	濱田直希, Yuichi Nagata, 小林重信 and Isao Ono : BS-AWA: A More Scalable Adaptive Weighted Aggregation for Continuous Multiobjective Optimization, Transaction of the Japanese Society for Evolutionary Computation, Vol.5, No.1, 1-15, 2014. (Summary) This paper proposes a more scalable variant of Adaptive Weighted Aggregation (AWA) with respect to the number of objectives in continuous multiobjective optimization. AWA is a scalarization-based multi-start strategy for generating finite points that approximate the entire Pareto set and Pareto front, which is especially focused on many-objective problems (having four or more objectives). In our last study, we discussed a reasonable stopping criterion for AWA, the <em>representing iteration</em>, and analyzed the time and space complexity of AWA when the representing iteration is used as a stopping criterion. Theoretical and empirical results showed that the running time and memory consumption of AWA depends on the number of solutions found in the representing iteration, the <em>representing number</em>. Due to the factorial increase of the representing number for objectives, the applicability of AWA is limited to 16-objective problems. In this study, we therefore redesign two central operations in AWA, the <em>subdivision</em> and the <em>relocation</em>, in order to reduce the representing number. The new subdivision is based on the simplicial complex and its barycentric subdivision and the new relocation is based on the simplicial approximation of a mapping and its range, both of which are well-known notions in topology. We theoretically compare the new AWA, named the <em>barycentric subdivision-based AWA (BS-AWA)</em>, with the old AWA in terms of their representing iteration, representing number and approximate memory consumption to illustrate the improvement of scalability; the result implies that BS-AWA is applicable to over 20-objective problems. Numerical experiments using 2- to 17-objective benchmark problems show that BS-AWA achieves a better coverage of obtained solutions than conventional multi-start descent methods in both the variable and objective spaces. The running time and the solution distribution of BS-AWA are also discussed. (Keyword) multi-objective optimization / continuous optimization / scalarization / multi-start search / weight adaptation (Link to Publication Site) ● Publication site (DOI): 10.11394/tjpnsec.5.1 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390282680342337152 ● Search Scopus @ Elsevier (DOI): 10.11394/tjpnsec.5.1 (DOI: 10.11394/tjpnsec.5.1, CiNii: 1390282680342337152)
14.	福島信純, Yuichi Nagata, 小林重信 and Isao Ono : Distance-weighted Exponential Natural Evolution Strategyの提案と性能評価, Transaction of the Japanese Society for Evolutionary Computation, Vol.4, No.2, 57-73, 2013. (Link to Publication Site) ● Publication site (DOI): 10.11394/tjpnsec.4.57 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.11394/tjpnsec.4.57 (DOI: 10.11394/tjpnsec.4.57)
15.	Yuichi Nagata and Shigenobu Kobayashi : A Powerful Genetic Algorithm using Edge Assembling Crossover for the Traveling Salesman Problem, INFORMS Journal on Computing, Vol.25, No.2, 346-363, 2013. (Link to Publication Site) ● Publication site (DOI): 10.1287/ijoc.1120.0506 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1287/ijoc.1120.0506 (DOI: 10.1287/ijoc.1120.0506)
16.	濱田直希, Yuichi Nagata, 小林重信 and Isao Ono : On the Stopping Criterion of Adaptive Weighted Aggregation for Multiobjective Continuous Optimization, Transaction of the Japanese Society for Evolutionary Computation, Vol.4, No.1, 13-27, 2013. (Summary) 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 <em>representing iteration</em>, is proposed as a stopping criterion for AWA, and the number of solutions found by the representing iteration, named the <em>representing number</em>, 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. (Keyword) multi-objective optimization / continuous optimization / scalarization / multi-start search / weight adaptation (Link to Publication Site) ● Publication site (DOI): 10.11394/tjpnsec.4.13 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390282680343138944 ● Search Scopus @ Elsevier (DOI): 10.11394/tjpnsec.4.13 (DOI: 10.11394/tjpnsec.4.13, CiNii: 1390282680343138944)
17.	Kazuyuki Masutomi, Yuichi Nagata and Isao Ono : An Evolutionary Algorithm for Black-Box Chance-Constrained Function Optimization, Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.17, No.2, 272-282, 2013. (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84879329821 (Elsevier: Scopus)
18.	上村健人, 木下峻一, Yuichi Nagata, 小林重信 and Isao Ono : Big-valley Explorer: A Framework of Real-coded Genetic Algorithms for Multi-funnel Function Optimization, Transaction of the Japanese Society for Evolutionary Computation, Vol.4, No.1, 13-27, 2013. (Summary) 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. (Keyword) function optimization / multi-funnel function / real-coded genetic algorithms / ISM (Link to Publication Site) ● Publication site (DOI): 10.11394/tjpnsec.4.1 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390001205366429312 ● Search Scopus @ Elsevier (DOI): 10.11394/tjpnsec.4.1 (DOI: 10.11394/tjpnsec.4.1, CiNii: 1390001205366429312)
19.	Y. Akimoto, Yuichi Nagata, Isao Ono and S. Kobayashi : Theoretical Foundation for CMA-ES from Information Geometry Perspective, Algorithmica, Vol.64, No.4, 698-716, 2012. (Link to Publication Site) ● Publication site (DOI): 10.1007/s00453-011-9564-8 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84868358714 (DOI: 10.1007/s00453-011-9564-8, Elsevier: Scopus)
20.	濱田直希, Yuichi Nagata, 小林重信 and Isao Ono : 被覆度を考慮したマルチスタート法による多目的連続最適化, Transaction of the Japanese Society for Evolutionary Computation, Vol.3, No.2, 31-46, 2012. (Link to Publication Site) ● Publication site (DOI): 10.11394/tjpnsec.3.31 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.11394/tjpnsec.3.31 (DOI: 10.11394/tjpnsec.3.31)
21.	Yuichi Nagata and David Soler : A new genetic algorithm for the asymmetric traveling salesman problem, Expert Systems with Applications, Vol.39, No.10, 8947-8953, 2012. (Link to Publication Site) ● Publication site (DOI): 10.1016/j.eswa.2012.02.029 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1016/j.eswa.2012.02.029 (DOI: 10.1016/j.eswa.2012.02.029)
22.	宮前惇, Yuichi Nagata, Isao Ono and 小林重信 : 多峰性景観下での直接政策探索:重点サンプリングを用いたPopulation-based Policy Gradient法, Transaction of the Japanese Society for Evolutionary Computation, Vol.2, No.1, 1-11, 2011. (Link to Publication Site) ● Publication site (DOI): 10.11394/tjpnsec.2.1 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.11394/tjpnsec.2.1 (DOI: 10.11394/tjpnsec.2.1)
23.	A. Maki, Y. Akimoto, Yuichi Nagata, Nagata S. Kobayashi, E. Kobayashi, S. Shiotani, T. Ohsawa and N. Umeda : A new weather-routing system that accounts for ship stability based on a real-coded genetic algorithm, Journal of Marine Science and Technology, Vol.16, No.3, 311-322, 2011. (Link to Publication Site) ● Publication site (DOI): 10.1007/s00773-011-0128-z (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1007/s00773-011-0128-z (DOI: 10.1007/s00773-011-0128-z)
24.	O. Bräysy, E. Martinez, Yuichi Nagata and D. Soler : The mixed capacitated general routing problem with turn penalties, Expert Systems with Applications, Vol.38, No.10, 12954-12966, 2011. (Link to Publication Site) ● Publication site (DOI): 10.1016/j.eswa.2011.04.092 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1016/j.eswa.2011.04.092 (DOI: 10.1016/j.eswa.2011.04.092)
25.	藤井宏行, Yuichi Nagata, Isao Ono and 小林重信 : 光学的情報τを用いた移動ロボットの設計と評価, Journal of the Robotics Society of Japan, Vol.28, No.10, 1189-1200, 2010. (Link to Publication Site) ● Publication site (DOI): 10.7210/jrsj.28.1189 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.7210/jrsj.28.1189 (DOI: 10.7210/jrsj.28.1189)
26.	Yuichi Nagata, O. Bräysy and W. Dullaert : A Penalty-based Edge Assembly Memetic Algorithm for the Vehicle Routing Problem with Time Windows, Computers & Operations Research, Vol.37, No.4, 724-737, 2010. (Link to Publication Site) ● Publication site (DOI): 10.1016/j.cor.2009.06.022 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1016/j.cor.2009.06.022 (DOI: 10.1016/j.cor.2009.06.022)
27.	Yuichi Nagata, 小林重信 and 東条敏 : Efficient Local Search Limitation Strategies in Memetic Algorithm, Transactions of the Japanese Society for Artificial Intelligence, Vol.25, No.2, 299-310, 2010. (Summary) Applications of memetic algorithms (MAs) are usually computationally expensive. In this paper we suggest efficient search limiting strategies for local search used in MAs because local search is the most time consuming part of MAs. The suggested strategies are applied to a recently proposed powerful MA for the capacitated vehicle routing problem (CVRP). Experimental results on the well-known benchmarks show a significant speed-up of 80% in running time without worsening the solution quality. Moreover, the MA dominates state-of-the-art heuristics for the CVRP with respect to both the computation time and the solution quality. (Keyword) memetic algorithm / genetic local search / vehicle routing / local search (Link to Publication Site) ● Publication site (DOI): 10.1527/tjsai.25.299 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390001205109070848 ● Summary page in Scopus @ Elsevier: 2-s2.0-77955969553 (DOI: 10.1527/tjsai.25.299, CiNii: 1390001205109070848, Elsevier: Scopus)
28.	大嶋彈, 宮前惇, Yuichi Nagata, 小林重信, Isao Ono and 佐久間淳 : A New Real-coded Genetic Algorithm with an Adaptive Mating Selection for UV-landscapes, Transactions of the Japanese Society for Artificial Intelligence, Vol.25, No.2, 290-298, 2010. (Summary) The purpose of this paper is to propose a new real-coded genetic algorithm (RCGA) named Networked Genetic Algorithm (NGA) that intends to find multiple optima simultaneously in deceptive globally multimodal landscapes. Most current techniques such as <I>niching</I> for finding multiple optima take into account <I>big valley</I> landscapes or non-deceptive <I>globally multimodal</I> landscapes but not deceptive ones called <I>UV-landscapes</I>. Adaptive Neighboring Search (ANS) is a promising approach for finding multiple optima in UV-landscapes. ANS utilizes a restricted mating scheme with a crossover-like mutation in order to find optima in deceptive globally multimodal landscapes. However, ANS has a fundamental problem that it does not find all the optima simultaneously in many cases. NGA overcomes the problem by an adaptive parent-selection scheme and an improved crossover-like mutation. We show the effectiveness of NGA over ANS in terms of the number of detected optima in a single run on Fletcher and Powell functions as benchmark problems that are known to have multiple optima, ill-scaledness, and UV-landscapes. (Keyword) genetic algorithm / function optimization / UV-structure / global multimodality / networked genetic algorithm / deceptive problem (Link to Publication Site) ● Publication site (DOI): 10.1527/tjsai.25.290 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390282680085783680 ● Summary page in Scopus @ Elsevier: 2-s2.0-77955985682 (DOI: 10.1527/tjsai.25.290, CiNii: 1390282680085783680, Elsevier: Scopus)
29.	秋本洋平, Yuichi Nagata, 佐久間淳, 小野功 and 小林重信 : Analysis of The Behavior of MGG and JGG As A Selection Model for Real-coded Genetic Algorithms, Transactions of the Japanese Society for Artificial Intelligence, Vol.25, No.2, 281-289, 2010. (Summary) 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. (Keyword) function optimization / minimal generation gap / just generation gap (Link to Publication Site) ● Publication site (DOI): 10.1527/tjsai.25.281 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390001205109072384 ● Summary page in Scopus @ Elsevier: 2-s2.0-77955938081 (DOI: 10.1527/tjsai.25.281, CiNii: 1390001205109072384, Elsevier: Scopus)
30.	Yuichi Nagata and Olli Bräysy : Edge Assembly based Memetic Algorithm for the Capacitated Vehicle Routing Problem, Networks, Vol.54, No.4, 205-215, 2009. (Link to Publication Site) ● Publication site (DOI): 10.1002/net.20333 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-71949088204 (DOI: 10.1002/net.20333, Elsevier: Scopus)
31.	秋本洋平, Yuichi Nagata, 佐久間淳, 小野功 and 小林重信 : Proposal and Evaluation of Adaptive Real-coded Crossover AREX, Transactions of the Japanese Society for Artificial Intelligence, Vol.24, No.6, 446-458, 2009. (Summary) 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. (Keyword) function optimization / adaptation of expansion rate / crossover mean descent / adaptive real-coded ensemble crossover (Link to Publication Site) ● Publication site (DOI): 10.1527/tjsai.24.446 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390001205108974592 ● Summary page in Scopus @ Elsevier: 2-s2.0-70350128255 (DOI: 10.1527/tjsai.24.446, CiNii: 1390001205108974592, Elsevier: Scopus)
32.	Yuichi Nagata and Olli Bräysy : A Powerful Route Minimization heuristic for the Vehicle Routing Problem with Time Windows, Operations Research Letters, Vol.37, No.5, 333-338, 2009. (Link to Publication Site) ● Publication site (DOI): 10.1016/j.orl.2009.04.006 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-69549111047 (DOI: 10.1016/j.orl.2009.04.006, Elsevier: Scopus)
33.	Yuichi Nagata : An Adaptive Niching Genetic Algorithm using a niche size equalization mechanism, Transactions of the Japanese Society for Artificial Intelligence, Vol.24, No.1, 92-103, 2009. (Summary) 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. (Keyword) genetic algorithm / deceptive problem / niching method / job-shop scheduling problem (Link to Publication Site) ● Publication site (DOI): 10.1527/tjsai.24.92 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390282680083022592 ● Summary page in Scopus @ Elsevier: 2-s2.0-59349089864 (DOI: 10.1527/tjsai.24.92, CiNii: 1390282680083022592, Elsevier: Scopus)
34.	Yuichi Nagata : Fast Implementation of Genetic Algorithm by Localized EAX Crossover for the Traveling Salesman Problem, Transactions of the Japanese Society for Artificial Intelligence, Vol.22, No.5, 524-552, 2007. (Summary) 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. (Keyword) genetic algorithm / TSP / EAX / localized crossover / population diversity (Link to Publication Site) ● Publication site (DOI): 10.1527/tjsai.22.542 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390001205108326656 ● Search Scopus @ Elsevier (DOI): 10.1527/tjsai.22.542 (DOI: 10.1527/tjsai.22.542, CiNii: 1390001205108326656)
35.	Yuichi Nagata : New Approach of a Genetic Algorithm for TSP Using the Evaluation Function Considering Local Diversity Loss, Transactions of the Japanese Society for Artificial Intelligence, Vol.21, No.2, 195-204, 2006. (Summary) 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. (Keyword) genetic algorithm / TSP / EAX / local diversity loss / evaluation function (Link to Publication Site) ● Publication site (DOI): 10.1527/tjsai.21.195 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390282680083497216 ● Summary page in Scopus @ Elsevier: 2-s2.0-32044441664 (DOI: 10.1527/tjsai.21.195, CiNii: 1390282680083497216, Elsevier: Scopus)
36.	Yuichi Nagata and 小林重信 : 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, Journal of Japanese Society for Artificial Intelligence, Vol.14, No.5, 848-859, 1999.

Academic Paper (Unrefereed Paper):

1.	Yang Yifei, Zhang Chaofeng, Wang Wenbin, Haichuan YANG and Yuichi Nagata : A classification and improvement method of metaheuristic algorithms based on complex networks, Bulletin of Advanced Institute of Industrial Technology, No.17, 94-99, 2024.

Review, Commentary:

1.	Yuichi Nagata : 多点探索アルゴリズムの基礎と最前線, オペレーションズ・リサーチ, Vol.58, No.12, 708-715, Dec. 2013.

Proceeding of International Conference:

1.	Yuichi Nagata : Escherization with a Distance Function Focusing on the Similarity of Local Structure, Proceedings of the 15th International Conference on Parallel Problem Solving from Nature (PPSN 2018), LNCS 11101, 108-120, Sep. 2018. (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-319-99253-2_9 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85053597397 (DOI: 10.1007/978-3-319-99253-2_9, Elsevier: Scopus)
2.	Yuichi Nagata : Population Diversity Measures Based on Variable-Order Markov Models for the Traveling Salesman Problem, Proceedings of the 14th International Conference on Parallel Problem Solving from Nature (PPSN 2016), 973-983, Sep. 2016. (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-319-45823-6_91 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84988490266 (DOI: 10.1007/978-3-319-45823-6_91, Elsevier: Scopus)
3.	Yuichi Nagata and Isao Ono : Random Partial Neighborhood Search for University Course Timetabling Problem, Proceedings of the 13th International Conference on Parallel Problem Solving from Nature, 782-791, Sep. 2014.
4.	T. Sasaki, Yuichi Nagata and Isao Ono : Improving Estimation Accuracy of Particle Filter by Efficient Interpolation Based on Crossover, Proceedings of the SICE Annual Conference 2014, 1216-1221, Sep. 2014.
5.	Yuichi Nagata and Isao Ono : High-Order Sequence Entropies for Measuring Population Diversity in the Traveling Salesman Problem, Proceedings of the 13th European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP 2013), LNCS 7832, 179-190, Sep. 2013.
6.	Yuichi Nagata and Isao Ono : An Enhancement of Edge Assembly Crossover for the Capacitated Vehicle Routing Problem, Proceedings of the 10th Metaheuristics International Conference (MIC 2013), 243-245, Aug. 2013.
7.	Kazuma Honda, Yuichi Nagata and Isao Ono : A Parallel Genetic Algorithm with Edge Assembly Crossover for 100,000-City Scale TSPs, Proceedings of the 2013 Congress on Evolutionary Computation (CEC 2013), 1278-1285, Jun. 2013.
8.	Kazuyuki Masutomi, Yuichi Nagata and Isao Ono : Extending Distance-weighted Exponential Natural Evolution Strategy for Function Optimization in Uncertain Environments, Proceedings of the 2013 Congress on Evolutionary Computation (CEC 2013), 2122-2129, Jun. 2013.
9.	Kazuyuki Masutomi, Yuichi Nagata and Isao Ono : Extending Distance-weighted Exponential Natural Evolution Strategy for Function Optimization in Uncertain Environments, Proceedings of the 2013 Congress on Evolutionary Computation (CEC 2013), 2122-2129, Jun. 2013.
10.	Kento Uemura, Naotoshi Nakashima, Yuichi Nagata and Isao Ono : A New Real-coded Genetic Algorithm for Implicit Constrained Black-box Function Optimization, Proceedings of the 2013 Congress on Evolutionary Computation (CEC 2013), 2887-2894, Jun. 2013.
11.	Ryo Miyazaki, Naoki Hamada, Yuichi Nagata and Isao Ono : A New Pareto Frontier Covering Strategy in FS-MOGA for Multi-Objective Function Optimization, Proceedings of the 6th International Conference on Soft Computing and Intelligent Systems, 1-6, Nov. 2012.
12.	Kazuma Masutomi, Yuichi Nagata and Isao Ono : An Evolutionary Algorithm for Chance-Constrained Function Optimization with Implicit Constraints, Proceedings of the International Symposium on Soft Computing sponsored by ASPIRE LEAGUE, 1-6, 2012.
13.	Yuichi Nagata, Isao Ono and Shigenobu Kobayashi : Memetic Algorithm using Selective Route Exchange Crossover for the Capacitated Vehicle Routing Problem, Proceedings of the 9th Metaheuristics International Conference (MIC 2011), 329-338, Jul. 2011.
14.	Kento Uemura, Shunichi Kinoshita, Yuichi Nagata, Shigenobu Kobayashi and Isao Ono : A New Framework taking account of Multi-funnel Functions for Real-coded Genetic Algorithms, Proceedings of the 2011 Congress on Evolutionary Computation (CEC 2011), 2091-2098, Jun. 2011.
15.	Nobusumi Fukushima, Yuichi Nagata, Shigenobu Kobayashi and Isao Ono : Proposal of Distance-weighted Exponential Natural Evolution Strategies, Proceedings of the 2011 Congress on Evolutionary Computation (CEC 2011), 164-171, Jun. 2011.

Proceeding of Domestic Conference:

1.	Hirotsuna Kutsuki and Yuichi Nagata : 深層強化学習を用いたシューティングゲーム AI の開発及び改善手法, 進化計算シンポジウム 2022, Dec. 2022.
2.	Takanori Homma and Yuichi Nagata : Sim-to-Real 学習に向けた強化学習による四足ロボットの歩行動作獲得の実験と考察, 進化計算シンポジウム 2022, Dec. 2022.
3.	西久保雅人 and Yuichi Nagata : 多変量混合正規分布を用いたCMA-ESの提案とその性能評価, 第21回進化計算学会研究会, Mar. 2022.
4.	84. 岡田直也 and Yuichi Nagata : AlphaZeroを用いた自己対戦による七並べゲームプレイヤの作成, 第21回進化計算学会研究会, Mar. 2022.
5.	83. 亘海都 and Yuichi Nagata : 層学習エージェントを用いた自己対戦によるコンピュータ大貧民の学習, 進化計算シンポジウム 2021, Dec. 2021.
6.	82. 居村亮治 and Yuichi Nagata : バネモデルを用いた目標図形の自然な変形によるエッシャータイリングの生成法, ICE四国支部学術講演会, Dec. 2021.
7.	81. 米田和弘 and Yuichi Nagata : 深層強化学習を用いたロボット制御法の内発的報酬による学習改善, ICE四国支部学術講演会, Dec. 2021.
8.	松本稔 and Yuichi Nagata : GAによる強い宝くじ仮説に基づいたNNの枝刈りに関する実験的考察, 電気学会C部門大会2021, Sep. 2021.
9.	Yuichi Nagata : --, 電気学会システム研究会, Jun. 2021.
10.	Yuichi Nagata : 遺伝的アルゴリズムによる絵画的迷路作成迷路の作成, 電気学会システム研究会, Mar. 2020.
11.	Yuta Yamami, Yuichi Nagata and Norihiko Ono : 個別的な突然変異による進化型深層強化学習, 2019年度計測自動制御学会関西支部・システム制御情報学会シンポジウム, Jan. 2020.
12.	Minoru Matsumoto, Yuichi Nagata and Norihiko Ono : ガウスベルヌーイ制限付きボルツマンマシンのエネルギー関数による最適化に関する考察, 2019年度計測自動制御学会関西支部・システム制御情報学会シンポジウム, Jan. 2020.
13.	Takumi Ishikawa, Yuichi Nagata and Norihiko Ono : On Optimization of Recurrent Neural Network Topologies along with Weights using CMA-ES, 第46回知能システムシンポジウム資料, Mar. 2019.
14.	Naruki Kawagishi, Naoya Okada, Yuichi Nagata and Norihiko Ono : Extraction of effective game record and learning of high quality simulation policy based on self-play by Monte Carlo Daihinmin players, 第46回知能システムシンポジウム資料, Mar. 2019.
15.	Masatoshi Abe, Yuichi Nagata and Norihiko Ono : A High Quality Training Data Generation Method for Effective Learning of Simulation Policy Focusing on the Best Hand on Game Tree, 第31回自律分散システム・シンポジウム資料, Jan. 2019.
16.	Ryota Shimomura, Yuichi Nagata and Norihiko Ono : Learning of Effective Strategies for One-on-one Battle Games by Co-evolutionary CMA-ES, 第31回自律分散システム・シンポジウム資料, Jan. 2019.
17.	Yuichi Nagata, 今宮明則 and Norihiko Ono : 絵画的迷路生成問題に対する遺伝的アルゴリズムの開発と最長経路問題への適用, 2018進化計算シンポジウム, Dec. 2018.
18.	Masateru Shimodaira, Yuichi Nagata and Norihiko Ono : A Topology and Weight Evolving Artificial Neural Network ANS-TWEANN and Its Application to the Pursuit Problem, 平成29年度計測自動制御学会関西支部・システム制御情報学会若手研究発表会, Jan. 2018.
19.	Toshihide Haga, Yuichi Nagata and Norihiko Ono : Acquisition of Simulation Policy by Bonanza Method and Its Application to Monte Carlo Daihinmin, 平成29年度計測自動制御学会関西支部・システム制御情報学会若手研究発表会, Jan. 2018.
20.	Kenichi Tomita, Yuichi Nagata and Norihiko Ono : Acquisition of Effective Strategies for One-on-one Competitive Games by Co-evolutionary Niching Generation Alternation Models, 平成29年度計測自動制御学会関西支部・システム制御情報学会若手研究発表会, Jan. 2018.
21.	Takuya Yokoi, Shimomura Ryota, Yuichi Nagata and Norihiko Ono : On niching genetic algorithms aimed at the evolution of neural network agents, 平成29年度計測自動制御学会関西支部・システム制御情報学会若手研究発表会, Jan. 2018.
22.	Yudai Oda, Norihiko Ono and Yuichi Nagata : 車両配送問題における追加注文を考慮した配送計画システムの提案, 第12回コンピューテーショナル・インテリジェンス研究会, Dec. 2017.
23.	Akinori Imamiya, Norihiko Ono and Yuichi Nagata : MAを用いた絵画的迷路の自動生成, 第12回コンピューテーショナル・インテリジェンス研究会, Dec. 2017.
24.	Yuichi Nagata : 一般化距離尺度を用いたエッシャー風タイリング問題の網羅的解法, 計算学会進化計算シンポジウム2017, Dec. 2017.
25.	山吹卓也, Norihiko Ono and Yuichi Nagata : 同卓スケジューリング問題のモデル化とメタ戦略を用いた近似解法の開発, 第29回自律分散システム・シンポジウム, Jan. 2017.
26.	田浦拓, Norihiko Ono and Yuichi Nagata : 進化計算を用いた非球面カメラレンズ設計, 第29回自律分散システム・シンポジウム, Jan. 2017.
27.	Yuichi Nagata : 可変長マルコフモデルに基づく巡回セールスマン問題に対する GA の多様性指標の提案, 進化計算学会進化計算シンポジウム2016, Dec. 2016.
28.	寺尾圭一郎, Norihiko Ono and Yuichi Nagata : 差分データを用いた巡回セールスマン問題のための GA-EAX の効率的並列化, 進化計算学会進化計算シンポジウム2016, Dec. 2016.
29.	西村悠哉, Norihiko Ono and Yuichi Nagata : 解構造のシームレスな変異と集団の多様性維持に基づくリカレントニューラルネットの進化的設計, 進化計算学会進化計算シンポジウム2016, Dec. 2016.
30.	今宮明則, Norihiko Ono and Yuichi Nagata : GA を用いた絵画的迷路の自動生成, 進化計算学会進化計算シンポジウム2016, Dec. 2016.
31.	沖展彰, Norihiko Ono and Yuichi Nagata : 実問題制約付き車両配送問題に対する配送計画システムの提案, 進化計算学会進化計算シンポジウム2016, Dec. 2016.
32.	Makoto Saitoh, Norihiko Ono and Yuichi Nagata : A Niching GA for Optimization Problems with Deceptive Structure, 計測自動制御学会システム・情報部門学術講演会 2016, Dec. 2016.
33.	寺尾圭一郎, Norihiko Ono and Yuichi Nagata : 大規模巡回セールスマン問題に対する交叉EAXを用いた遺伝的アルゴリズムの並列化, 計測自動制御学会システム・情報部門学術講演会2015, Nov. 2015.
34.	Yuichi Nagata : 高次の依存関係を考慮したエントロピー指標による遺伝的アルゴリズムの多様性維持, 計測自動制御学会システム・情報部門学術講演会2015, Nov. 2015.
35.	Kohsuke Nishino, Norihiko Ono and Yuichi Nagata : An Framework for PORTS-based Strongly Typed Genetic Programming, 計測自動制御学会第42回知能システムシンポジウム資料, Mar. 2015.
36.	Toshihito Horita, Yuya Nishimura, Norihiko Ono and Yuichi Nagata : Evolutionary Design of Recurrent Neural Network Topologies Along with Weights Based on Seamless Topology Mutations and An Adaptive Niching Generation Alternation Model, 計測自動制御学会第42回知能システムシンポジウム資料, Mar. 2015.
37.	上村健人, Yuichi Nagata and Isao Ono : 非明示アクティブ制約と稜構造を考慮した実数値 GA の提案, 進化計算学会進化計算シンポジウム2014, Dec. 2014.
38.	Yuichi Nagata : 部分ランダム近傍を用いた大学時間割作成問題の解法, 進化計算学会進化計算シンポジウム2014, Dec. 2014.
39.	山越幸太, Yuichi Nagata and Isao Ono : TSP のためのGA-EAX における探索ステージ切換条件に関する一検討, 計測自動制御学会システム・情報部門学術講演会2014, Nov. 2014.
40.	Yuichi Nagata : Memetic Algorithmを用いたVehicle Routing Problemの効率的近似解法, 第11回OR学会中部支部シンポジウム, Sep. 2014.
41.	Yuichi Nagata : 多点探索の最前線, 日本オペレーションズ・リサーチ学会 2014年秋季シンポジウム, Aug. 2014.

Grants-in-Aid for Scientific Research (KAKEN Grants Database @ NII.ac.jp)

計算に基づくエッシャータイリングの深化 (Project/Area Number: 24K14842 )
プロクラステス距離の一般化を軸としたエッシャータイリング自動生成法の深化 (Project/Area Number: 20K11695 )
Constraint oriented metaheuristics system for the vehicle routing problem (Project/Area Number: 17K00342 )
Automatic construction of mataheuristic algorithms for various vehicle routing problems (Project/Area Number: 25330284 )
The development of iterative constraint satisfaction problem solving methods for combinational optimization problems (Project/Area Number: 22700231 )
Genetic algorithm for very large traveling salesman problems and its applications to practical applications (Project/Area Number: 19700134 )
遺伝的アルゴリズムを用いた近似最適化手法の一般的設計指針と応用のための基礎的研究 (Project/Area Number: 14780266 )
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Professor : Nagata, Yuichi

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Grants-in-Aid for Scientific Research (KAKEN Grants Database @ NII.ac.jp)