Optimisation of the high-order problems in evolutionary algorithms: an application of transfer learning

Hao, Guo; Wang, Gai-Ge; Zhang, Zhao Jun; Zou, De Xuan

doi:10.1504/ijwmc.2018.089991

Cited by 1 publication

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“…More fundamentally, we can try to modify the basic structure of the MTEC algorithm [185,186]. For instance, Chen et al [129] introduced a local search strategy based on quasi-Newton, a re-initialization technique of worse individuals, and a self-adapt parent selection strategy to obtain better solutions.…”

Section: Enhance Effectiveness and Efficiency Of Mtec Algorithmsmentioning

confidence: 99%

Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief Review

Wang

et al. 2021

Mathematics

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Traditional evolution algorithms tend to start the search from scratch. However, real-world problems seldom exist in isolation and humans effectively manage and execute multiple tasks at the same time. Inspired by this concept, the paradigm of multi-task evolutionary computation (MTEC) has recently emerged as an effective means of facilitating implicit or explicit knowledge transfer across optimization tasks, thereby potentially accelerating convergence and improving the quality of solutions for multi-task optimization problems. An increasing number of works have thus been proposed since 2016. The authors collect the abundant specialized literature related to this novel optimization paradigm that was published in the past five years. The quantity of papers, the nationality of authors, and the important professional publications are analyzed by a statistical method. As a survey on state-of-the-art of research on this topic, this review article covers basic concepts, theoretical foundation, basic implementation approaches of MTEC, related extension issues of MTEC, and typical application fields in science and engineering. In particular, several approaches of chromosome encoding and decoding, intro-population reproduction, inter-population reproduction, and evaluation and selection are reviewed when developing an effective MTEC algorithm. A number of open challenges to date, along with promising directions that can be undertaken to help move it forward in the future, are also discussed according to the current state. The principal purpose is to provide a comprehensive review and examination of MTEC for researchers in this community, as well as promote more practitioners working in the related fields to be involved in this fascinating territory.

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Section: Enhance Effectiveness and Efficiency Of Mtec Algorithmsmentioning

confidence: 99%

Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief Review

Wang

et al. 2021

Mathematics

View full text Add to dashboard Cite

show abstract

Optimisation of the high-order problems in evolutionary algorithms: an application of transfer learning

Cited by 1 publication

References 0 publications

Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief Review

Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief Review

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