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ORCIDHiroaki Fukumoto
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2020 – today
- 2020
[c8]Yohanes Bimo Dwianto
, Hiroaki Fukumoto, Akira Oyama:
Adaptively preserving solutions in both feasible and infeasible regions on generalized multiple constraint ranking. GECCO 2020: 690-698
2010 – 2019
- 2019
- [c7]Hiroaki Fukumoto, Akira Oyama:
Coverage Enhancement of MOEA/D-M2M for Problems with Difficult-to-Approximate Pareto Front Boundaries. CEC 2019: 1734-1741 - [c6]Tatsumasa Ishikawa, Hiroaki Fukumoto, Akira Oyama, Hiroyuki Nishida:
Improved binary additive epsilon indicator for obtaining uniformly distributed solutions in multi-objective optimization. GECCO (Companion) 2019: 209-210 - [c5]Yohanes Bimo Dwianto, Hiroaki Fukumoto, Akira Oyama:
On improving the constraint-handling performance with modified multiple constraint ranking (MCR-mod) for engineering design optimization problems solved by evolutionary algorithms. GECCO 2019: 762-770 - [c4]Hiroaki Fukumoto, Akira Oyama:
Impact of Estimation Method of Ideal/Nadir Points on Practically-Constrained Multi-Objective Optimization Problems for Decomposition-Based Multi-Objective Evolutionary Algorithm. SSCI 2019: 2138-2145 - 2018
- [c3]Hiroaki Fukumoto, Akira Oyama:
A Generic Framework for Incorporating Constraint Handling Techniques into Multi-Objective Evolutionary Algorithms. EvoApplications 2018: 634-649 - [c2]Hiroaki Fukumoto, Akira Oyama:
Benchmarking multiobjective evolutionary algorithms and constraint handling techniques on a real-world car structure design optimization benchmark problem. GECCO (Companion) 2018: 177-178 - [c1]Hiroaki Fukumoto, Akira Oyama:
Study on Improving Efficiency of Multi-Objective Evolutionary Algorithm with Large Population by M2M Decomposition and Elitist Mate Selection Scheme. SSCI 2018: 1180-1187
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