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Controlling Dominance Area of Solutions and Its Impact on the Performance of MOEAs

It is shown that either convergence or diversity can be emphasized by contracting or expanding the dominance area, and that the optimal value of the area of dominance depends strongly on all factors analyzed here: number of objectives, size of the search space, and complexity of the problems.
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Ceci n'est pas une pipe: A deep convolutional network for fine-art paintings classification

This paper trains an end-to-end deep convolution model to investigate the capability of the deep model in fine-art painting classification problem and employs the recently publicly available large-scale “Wikiart paintings” dataset that consists of more than 80,000 paintings.
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Working principles, behavior, and performance of MOEAs on MNK-landscapes

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Computational Cost Reduction of Nondominated Sorting Using the M-Front

Experimental results show that the proposed M-front data structure can perform significantly faster than the state-of-the-art Jensen-Fortin's algorithm, especially in many-objective scenarios.
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ArtGAN: Artwork synthesis with conditional categorical GANs

The proposed ArtGAN is capable to create realistic artwork, as well as generate compelling real world images that globally look natural with clear shape on CIFAR-10.
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Objective space partitioning using conflict information for solving many-objective problems

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Injecting CMA-ES into MOEA/D

This work relies on the ability of CMA-ES to deal with injected solutions in order to update different covariance matrices with respect to each subproblem defined in MOEA/D, and shows that by cooperatively evolving neighboring C MA-ES components, it is able to obtain competitive results for different multi-objective benchmark functions.
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Self-Controlling Dominance Area of Solutions in Evolutionary Many-Objective Optimization

Simulation results show that SCDAS achieves well-balanced search performance on both convergence and diversity compared to conventional NSGA-II, CDAS, IBEAe+ and MSOPS.
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Local dominance using polar coordinates to enhance multiobjective evolutionary algorithms

The effectiveness of the proposed method obtaining Pareto optimal solutions satisfying diversity conditions is verified by comparing the search performance between the conventional algorithms and their enhanced versions.
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Effects of δ-Similar Elimination and Controlled Elitism in the NSGA-II Multiobjective Evolutionary Algorithm

In this paper, we propose <S-similar elimination to induce a better distribution of non-dominated solutions and distribute more fairly selection pressure among them in order to improve the search…
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