Parameter analysis on multi‐factorial evolutionary algorithm

Xu, Qingzheng; Zhang, Jianhang; Fei, Rong; Li, Wei

doi:10.1049/joe.2019.1230

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…On one hand, the inductive biases transferred from another task are helpful to effectively accelerate convergence. On the other hand, excessive inter-population reproduction may lead to negative genetic transfer across tasks and bad algorithm performance [11,118]. Thus, a natural question in multi-task optimization community is finding a proper balance between intra-population reproduction and inter-population reproduction [51].…”

Section: Balance Between Intra-population Reproduction and Inter-population Reproductionmentioning

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: Balance Between Intra-population Reproduction and Inter-population Reproductionmentioning

confidence: 99%

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

Wang

et al. 2021

Mathematics

Self Cite

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“…Adaptive: [9,12,18,28,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70] [ 20,27,31,74,75,76,77,78,79,80,81,…”

Section: Staticmentioning

confidence: 99%

“…Main objectives of that study are twofold: to theoretically unveil why MFEA based methods perform better that classical techniques, and to provide some findings on the parameter setup of MFEA algorithm. In the recent [36], the impact of three different MFEA parameters is analyzed: probability of individual learning, probability of intra-crossover and probability of intercrossover. In [37], a rigorous analysis is carried out on the relationship of MFEA and the conceptually similar multipopulation evolution models.…”

Section: Staticmentioning

confidence: 99%

Evolutionary Multitask Optimization: a Methodological Overview, Challenges and Future Research Directions

Osaba¹,

Martinez²,

Ser³

2021

Preprint

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In this work we consider multitasking in the context of solving multiple optimization problems simultaneously by conducting a single search process. The principal goal when dealing with this scenario is to dynamically exploit the existing complementarities among the problems (tasks) being optimized, helping each other through the exchange of valuable knowledge. Additionally, the emerging paradigm of Evolutionary Multitasking tackles multitask optimization scenarios by using as inspiration concepts drawn from Evolutionary Computation. The main purpose of this survey is to collect, organize and critically examine the abundant literature published so far in Evolutionary Multitasking, with an emphasis on the methodological patterns followed when designing new algorithmic proposals in this area (namely, multifactorial optimization and multipopulation-based multitasking). We complement our critical analysis with an identification of challenges that remain open to date, along with promising research directions that can stimulate future efforts in this topic. Our discussions held throughout this manuscript are offered to the audience as a reference of the general trajectory followed by the community working in this field in recent times, as well as a self-contained entry point for newcomers and researchers interested to join this exciting research avenue.

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Evolutionary Multitask Optimization: a Methodological Overview, Challenges, and Future Research Directions

et al. 2022

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Parameter analysis on multi‐factorial evolutionary algorithm

Cited by 4 publications

References 16 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

Evolutionary Multitask Optimization: a Methodological Overview, Challenges and Future Research Directions

Evolutionary Multitask Optimization: a Methodological Overview, Challenges, and Future Research Directions

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