A Generic Framework for Incorporating Constraint Handling Techniques into Multi-Objective Evolutionary Algorithms. - Hiroaki Fukumoto; Akira Oyama (EvoApplications, 2018)

Benchmarking multiobjective evolutionary algorithms and constraint handling techniques on a real-world car structure design optimization benchmark problem. - Hiroaki Fukumoto; Akira Oyama (GECCO (Companion), 2018)

Study on Improving Efficiency of Multi-Objective Evolutionary Algorithm with Large Population by M2M Decomposition and Elitist Mate Selection Scheme. - Hiroaki Fukumoto; Akira Oyama (SSCI, 2018)

Coverage Enhancement of MOEA/D-M2M for Problems with Difficult-to-Approximate Pareto Front Boundaries. - Hiroaki Fukumoto; Akira Oyama (CEC, 2019)

On improving the constraint-handling performance with modified multiple constraint ranking (MCR-mod) for engineering design optimization problems solved by evolutionary algorithms. - Yohanes Bimo Dwianto; Hiroaki Fukumoto; Akira Oyama (GECCO, 2019)

Improved binary additive epsilon indicator for obtaining uniformly distributed solutions in multi-objective optimization. - Tatsumasa Ishikawa; Hiroaki Fukumoto; Akira Oyama; Hiroyuki Nishida (GECCO (Companion), 2019)

Impact of Estimation Method of Ideal/Nadir Points on Practically-Constrained Multi-Objective Optimization Problems for Decomposition-Based Multi-Objective Evolutionary Algorithm. - Hiroaki Fukumoto; Akira Oyama (SSCI, 2019)

Adaptively preserving solutions in both feasible and infeasible regions on generalized multiple constraint ranking. - Yohanes Bimo Dwianto; Hiroaki Fukumoto; Akira Oyama (GECCO, 2020)