Analysis on the Efficiency of Multifactorial Evolutionary Algorithms

Huang, Zhengxin; Chen, Zefeng; Zhou, Yuejiao

doi:10.1007/978-3-030-58115-2_44

Cited by 8 publications

(5 citation statements)

References 22 publications

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“…Evolutionary multitasking builds on the notion of related tasks sharing reusable building-blocks of knowledge, harnessing the rich body of information gathered while optimizing tasks within the same domain [47,48]. By transferring useful information across joint optimization processes, it becomes possible to simplify the search, thus speeding up convergence rates to near-optimal solutions [49,50].…”

Section: One-pass Neuroevolutionary Multitaskingmentioning

confidence: 99%

Jack and Masters of All Trades: One-Pass Learning of a Set of Model Sets from Foundation Models

Choong¹,

Ong²,

Gupta³

et al. 2022

Preprint

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For deep learning, size is power. Massive neural nets trained on broad data for a spectrum of tasks are at the forefront of artificial intelligence. These foundation models or 'Jacks of All Trades' (JATs), when fine-tuned for downstream tasks, are gaining importance in driving deep learning advancements. However, environments with tight resource constraints, changing objectives and intentions, or varied task requirements, could limit the real-world utility of a singular JAT. Hence, in tandem with current trends towards building increasingly large JATs, this paper conducts an initial exploration into concepts underlying the creation of a diverse set of compact machine learning model sets. Composed of many smaller and specialized models, we formulate the Set of Sets to simultaneously fulfil many task settings and environmental conditions. A means to arrive at such a set tractably in one pass of a neuroevolutionary multitasking algorithm is presented for the first time, bringing us closer to models that are collectively 'Masters of All Trades'.

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Section: One-pass Neuroevolutionary Multitaskingmentioning

confidence: 99%

Jack and Masters of All Trades: One-Pass Learning of a Set of Model Sets from Foundation Models

Choong¹,

Ong²,

Gupta³

et al. 2022

Preprint

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“…This theoretical result indicates that MTEC is probably a promising approach to deal with some distinct problems in the field of evolutionary computation. The proposed MFEA algorithm is further analyzed on several benchmark pseudo-Boolean functions [47]. Theoretical analysis results show that, by properly setting the parameter rmp for the group of problems with similar tasks, the upper bound of expected runtime of (4 + 2) MFEA on the harder task can be improved to be the same as on the easier one, while for the group of problems with dissimilar tasks, the expected upper bound of (4 + 2) MFEA on each task are the same as that of solving them independently.…”

Section: Theoretical Analyses Of Multi-task Evolutionary Computationmentioning

confidence: 99%

“…On the other hand, the researchers and practitioners ignore further study on the theoretic analysis of MTO and MTEC, either consciously or unconsciously. The most representative results focused on convergence performance [37,41] and time complexity [46,47] of simplified MFEA, which theoretically explains the superiority of the MTEC algorithm compared with traditional single-task EAs. Comparatively speaking, other theoretical analysis (stability, diversity, etc.)…”

Section: Balance Theoretical Analysis and Practical Applicationmentioning

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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“…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%

“…As part of the work carried out, there are several published papers that have also delved into theoretical aspects of these paradigms. In the recent [35], for example, an analysis on the efficiency of MFEA is carried out. 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.…”

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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Analysis on the Efficiency of Multifactorial Evolutionary Algorithms

Cited by 8 publications

References 22 publications

Jack and Masters of All Trades: One-Pass Learning of a Set of Model Sets from Foundation Models

Jack and Masters of All Trades: One-Pass Learning of a Set of Model Sets from Foundation Models

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

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