New Computational Intelligence Findings from Hebei Agricultural University Discu ssed (A Generative Adversarial Networks Model Based Evolutionary Algorithm for M ultimodal Multi-objective Optimization)
New Computational Intelligence Findings from Hebei Agricultural University Discu ssed (A Generative Adversarial Networks Model Based Evolutionary Algorithm for M ultimodal Multi-objective Optimization)
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on Machine Learning - Compu tational Intelligence have been published. According to news reporting from Baod ing, People's Republic of China, by NewsRx journalists, research stated, "The ke y to solving multimodal multi-objective optimization problems is to achieve good diversity in the decision space. However, the existing algorithms usually adopt the reproduction operation based on random mechanism, which do not make full us e of the distribution features of promising solutions in the population, resulti ng in the defects of the diversity of the obtained Parteo optimal solution sets. " Financial support for this research came from National Natural Science Foundatio n of China (NSFC). The news correspondents obtained a quote from the research from Hebei Agricultur al University, "In order to solve the above problem, this paper proposes a multi modal multi-objective optimization evolutionary algorithm (MMOEA) based on gener ative adversarial networks (GANs). Specifically, we firstly design a classificat ion strategy to distinguish good solutions from poor solutions. The solutions in the population are classified as real samples and fake samples by non-dominated selection sorting based on special crowding distance, and the training data of GANs are obtained. Secondly, a GANs-based offspring generation method is propose d. Through the adversarial training of GANs, the generator can simulate the dist ribution of promising solutions in the population and generate offspring with go od diversity. Thirdly, an environment selection strategy based on GANs is constr ucted. By sorting the classification probability of the solutions output by the discriminator, the population are selected and updated."
Key words
Baoding/People's Republic of China/Asi a/Computational Intelligence/Machine Learning/Algorithms/Evolutionary Algori thm/Mathematics/Hebei Agricultural University
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