Author
Mitsuo Gen
Other affiliations: Ashikaga Institute of Technology, University of California, University of California, Berkeley ...read more
Bio: Mitsuo Gen is an academic researcher from Tokyo University of Science. The author has contributed to research in topics: Genetic algorithm & Job shop scheduling. The author has an hindex of 63, co-authored 472 publications receiving 16818 citations. Previous affiliations of Mitsuo Gen include Ashikaga Institute of Technology & University of California.
Papers published on a yearly basis
Papers
More filters
17 Dec 1999
TL;DR: This paper presents a meta-modelling framework that automates the very labor-intensive and therefore time-heavy and therefore expensive and labor-heavy process of designing and solving optimization problems.
Abstract: Foundations of Genetic Algorithms. Combinatorial Optimization Problems. Multiobjective Optimization Problems. Fuzzy Optimization Problems. Reliability Design Problems. Scheduling Problems. Advanced Transportation Problems. Network Design and Routing. Manufacturing Cell Design. References. Index.
2,348 citations
TL;DR: A tutorial survey of recent works on solving classical JSP using genetic algorithms using various hybrid approaches of genetic algorithms and conventional heuristics is given.
639 citations
TL;DR: A new solution procedure based on genetic algorithms to find the set of Pareto-optimal solutions for multi-objective SCN design problem and two different weight approaches are implemented in the proposed solution procedure.
555 citations
TL;DR: This paper developed a hybrid genetic algorithm (GA) that uses two vectors to represent solutions and developed an efficient method to find assignable time intervals for the deleted operations based on the concept of earliest and latest event time.
470 citations
Book•
[...]
01 Oct 1999
TL;DR: This important book addresses one of the most important optimization techniques in the industrial engineering/manufacturing area, the use of genetic algorithms to better design and produce reliable products of high quality.
Abstract: From the Publisher:
Genetic algorithms are probabilistic search techniques based on the principles of biological evolution. As a biological organism evolves to more fully adapt to its environment, a genetic algorithm follows a path of analysis from which a design evolves, one that is optimal for the environmental constraints placed upon it. Written by two internationally-known experts on genetic algorithms and artificial intelligence, this important book addresses one of the most important optimization techniques in the industrial engineering/manufacturing area, the use of genetic algorithms to better design and produce reliable products of high quality. The book covers advanced optimization techniques as applied to manufacturing and industrial engineering processes, focusing on combinatorial and multiple-objective optimization problems that are most encountered in industry.
390 citations
Cited by
More filters
[...]
TL;DR: The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.
10,082 citations
TL;DR: Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods.
7,090 citations
TL;DR: Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjectives optimization problems.
Abstract: Decomposition is a basic strategy in traditional multiobjective optimization. However, it has not yet been widely used in multiobjective evolutionary optimization. This paper proposes a multiobjective evolutionary algorithm based on decomposition (MOEA/D). It decomposes a multiobjective optimization problem into a number of scalar optimization subproblems and optimizes them simultaneously. Each subproblem is optimized by only using information from its several neighboring subproblems, which makes MOEA/D have lower computational complexity at each generation than MOGLS and nondominated sorting genetic algorithm II (NSGA-II). Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjective optimization problems. It has been shown that MOEA/D using objective normalization can deal with disparately-scaled objectives, and MOEA/D with an advanced decomposition method can generate a set of very evenly distributed solutions for 3-objective test instances. The ability of MOEA/D with small population, the scalability and sensitivity of MOEA/D have also been experimentally investigated in this paper.
6,657 citations
Book•
30 Jun 2002
TL;DR: This paper presents a meta-anatomy of the multi-Criteria Decision Making process, which aims to provide a scaffolding for the future development of multi-criteria decision-making systems.
Abstract: List of Figures. List of Tables. Preface. Foreword. 1. Basic Concepts. 2. Evolutionary Algorithm MOP Approaches. 3. MOEA Test Suites. 4. MOEA Testing and Analysis. 5. MOEA Theory and Issues. 3. MOEA Theoretical Issues. 6. Applications. 7. MOEA Parallelization. 8. Multi-Criteria Decision Making. 9. Special Topics. 10. Epilog. Appendix A: MOEA Classification and Technique Analysis. Appendix B: MOPs in the Literature. Appendix C: Ptrue & PFtrue for Selected Numeric MOPs. Appendix D: Ptrue & PFtrue for Side-Constrained MOPs. Appendix E: MOEA Software Availability. Appendix F: MOEA-Related Information. Index. References.
5,994 citations