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The official implementation of GMOEA [1] with Pytorch: is the main file. is the GAN model used in GMOEA. IMF test problems is the class including the general parameters for running GMOEA. is the mating selection strategy used in GMOEA. Specifically, the mating selection strategy in RSEA [2] is used. is the environmental selection strategy used in GMOEA. Specifically, the environmental selection strategy in SPEA2 [3] is used. is the non-dominated sorting method using the efficient non-dominated sorting method in [4]. is the simulated binary crossover and polynomial mutation. is the polynomial mutation [5]. is the K-tournament selection [6].


[1] He C, Huang S, Cheng R, Tan KC, Jin Y. Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs). IEEE Transactions on Cybernetics. 2020 Apr 30.

[2]. He C, Tian Y, Jin Y, et al. A radial space division based evolutionary algorithm for many-objective optimization[J]. Applied Soft Computing, 2017, 61: 603-621.

[3]. E. Ziztler, M. Laumanns, and L. Thiele, “SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjective optimization,” Evolutionary Methods for Design, Optimization, and Control, pp. 95–100, 2002.

[4]. Zhang X, Tian Y, Cheng R, et al. An efficient approach to nondominated sorting for evolutionary multiobjective optimization. IEEE Transactions on Evolutionary Computation, 2015, 19(2): 201-213.

[5]. Deb K, Beyer H G. Self-adaptive genetic algorithms with simulated binary crossover. Secretary of the SFB 531, 1999.

[6]. Xie H, Zhang M. Tuning Selection Pressure in Tournament Selection[M]. School of Engineering and Computer Science, Victoria University of Wellington, 2009.


Official implementation of GMOEA.






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