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Effective knowledge transfer has been proven to achieve superior performance in evolutionary optimization. Evolutionary multitasking optimization (EMT), which can solve several optimization tasks simultaneously using evolutionary algorithms, continues to be a young research field but is growing rapidly. Due to the parallelism of population-based search, the performance of component tasks can be improved through effective knowledge transfer between different tasks. The main challenge in the EMT field is addressing the negative transfer. Aiming to overcome this challenge, this paper proposes an EMT algorithm that transfers effective knowledge through semi-supervised learning. In addition, a semi-supervised classification method is designed based on the cluster assumption, which is part of the geometric basis of semi-supervised learning. By using both labeled and unlabeled samples generated in the optimization process, the proposed method can identify individuals that contain valuable knowledge and select them to transfer the knowledge between tasks. In this way, the performance of the EMT algorithm can be significantly improved. The effectiveness of the proposed method is verified by empirical tests and comparison with two benchmarks. Further, a case study is conducted. The results indicate that the proposed algorithm can achieve highly competitive performance compared with the state-of-the-art EMT algorithms. [ABSTRACT FROM AUTHOR]

Multimodal multiobjective evolutionary algorithms are expected to search for not only Pareto optimal front but also the corresponding multiple equivalent Pareto optimal sets. To this end, this paper designs a framework to ameliorate the performance of the existing multiobjective evolutionary algorithms (MOEAs) for solving multimodal multiobjective optimization problems (MMOPs). In this framework, an MMOP is transformed into a bi-objective optimization problem. The first objective is built by either decomposition-based method or indicator-based method in MOEAs to ensure the population convergence. A diversity indicator is another objective used to preserve the population diversity. Based on these two objectives, two diversity selection strategies are developed, which are responsible for balancing the diversity and the convergence in the decision and objective spaces, respectively, at the same time. According to the feedback information in the evolution, our framework adaptively selects one of them to pick out the promising individuals. Four variants based on our framework are built and are evaluated on 22 MMOPs benchmark functions. Six feature selection problems are also employed to test the performance of our framework. The experimental results confirm that our proposed framework performs well in all criteria. [ABSTRACT FROM AUTHOR]

Feature selection (FS) in data mining and machine learning has attracted extensive attention. The purpose of FS in a classification task is to find the optimal subset of features from given candidate features. Recently, more and more meta-heuristic algorithms have been used to deal with the FS problems. However, meta-heuristic algorithms suffer from certain issues, such as large search space for solutions and huge time consumption. Moreover, most of existing meta-heuristic algorithms focus only on the selection of an optimal feature subset, and pay little attention to the optimal design of the classifier. In this article, we propose a joint multiobjective optimization method for both feature selection and classifier design, called JMO-FSCD. The proposed approach uses neural network as a classifier and introduces a non-iterative algorithm for training the classifier so as to ensure good performance and fast learning. A new coding scheme is also designed for optimizing FS and classifier simultaneously. For demonstrating the superiority of the proposed approach, its performance is compared with those of six state-of-the-art FS algorithms. Experimental results on thirty-five benchmark data sets reflect the superior performance of the proposed JMO-FSCD. [ABSTRACT FROM AUTHOR]