Evolutionary computation for many-objective optimization problems using massive population sizes on the K supercomputer
暂无分享,去创建一个
[1] Pierre Collet,et al. GPGPU-Compatible Archive Based Stochastic Ranking Evolutionary Algorithm (G-ASREA) for Multi-Objective Optimization , 2010, PPSN.
[2] Fumiyoshi Shoji,et al. The K computer: Japanese next-generation supercomputer development project , 2011, IEEE/ACM International Symposium on Low Power Electronics and Design.
[3] Akira Oyama,et al. A ranking method based on two preference criteria: Chebyshev function and ε-indicator , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).
[4] Qingfu Zhang,et al. MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.
[5] Richard Balling,et al. The Maximin Fitness Function; Multi-objective City and Regional Planning , 2003, EMO.
[6] Hisao Ishibuchi,et al. Evolutionary many-objective optimization by NSGA-II and MOEA/D with large populations , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.
[7] Kalyanmoy Deb,et al. An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints , 2014, IEEE Transactions on Evolutionary Computation.
[8] Kalyanmoy Deb,et al. A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..
[9] Marco Laumanns,et al. Performance assessment of multiobjective optimizers , 2002 .