A Multitasking Genetic Algorithm for Mamdani Fuzzy System with Fully Overlapping Triangle Membership Functions

Zhang, Ke; Hao, Wenning; Yu, Xiaohan; Jin, Dawei; Zhang, Zhonghui

doi:10.1007/s40815-020-00954-2

Cited by 13 publications

(7 citation statements)

References 39 publications

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“…For solving multiple optimization tasks of fuzzy system, the encoding and decoding scheme was proposed in [72]. Each individual comprises multiple chromosomes corresponding to every fuzzy variables of the fuzzy system.…”

Section: Chromosome Encoding and Decoding Schemementioning

confidence: 99%

“…Island-EMT [167] Examination timetabling problem EMHH [78] Graph coloring problem EMHH [78] Minimum inter-cluster routing cost clustered tree problem (InterCluMRCT) CC-MFEA [65] Clustered shortest path tree problem (CluSTP) None [62], None [64], CC-MFEA [65], N-MFEA [68], N-MFEA [70] Real-world problem Machine learning Time series prediction problem MFGP [61] Performance prediction problem None [168] Gene regulatory network (GRN) reconstruction MMMA-FCM [169] Community detection MUMI [73] Chaotic time series prediction problem HD-MFEA neuroevolution [145] Training deep neural networks (DNN) problem AMTO [170], None [171] Fuzzy cognitive map (FCM) learning MMMA-FCM [169] Symbolic regression problem (SRP) MFGP [61] Multi-classification problem mXOF [138], EMC-GEP [172] Binary classification problem MFGP [59] Automatic hyperparameter tuning of machine learning models TEMO-MPS [109] Fuzzy system optimization problem MTGFS [72] Association mining problem MFEA [76] Classification problem DMSPSO [89], PSO-EMT [173], MMT-ELM [174] Table 3. Cont.…”

Section: Domain Problem Algorithmsmentioning

confidence: 99%

“…The optimization problem of fuzzy systems is used to optimize the parameters or (and) structure of the fuzzy system. Zhang et al [72] presented a general framework of the multi-task genetic fuzzy system (MTGFS) to effectively solve this problem. For the sake of better searches in multiple optimization tasks, an efficient assortative mating method (a chromosome-based shuffling strategy and a cross-task bias estimation based on shuffling) was designed according to the specialty of the membership functions.…”

Section: Machine Learningmentioning

confidence: 99%

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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: Chromosome Encoding and Decoding Schemementioning

confidence: 99%

Section: Domain Problem Algorithmsmentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%

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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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“…More concretely, this method capitalizes the sharing of information by sporadically creating overlapping populations. Also interesting is the work proposed in [112]. In that work a specific instantiation of a multitasking genetic fuzzy system is presented and developed: a multitasking evolutionary optimization algorithm for Mamdani fuzzy systems with fully overlapping triangle membership functions (FOTMF-M-MTGFS).…”

Section: Explicit Knowledge Transfer Based Static Solversmentioning

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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“…The base of knowledge was a set of fuzzy rule controls, including linguistic labels, describing the knowledge of professional experts of the controlled method. Mamdani's fuzzy model, one of the most accepted procedures and algorithms, was used in this approach [51]. Normally, the premise of the inference rule is established as combinations of input membership functions-IF, and the consequence part of the rule is stated as the output membership functions-THEN.…”

Section: Knowledge Base and Inference Rulesmentioning

confidence: 99%

An Approach Concerning Climate Change and Timber Building Resilience: Araucanía Region, South Chile

2021

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This study analysed climate change effects concerning the resilience of timber buildings located in southern Chile, specifically in two cities: Collipulli and Temuco (Araucanía Region). A digital fuzzy logic method was used in a set of timber buildings declared as heritage conservation buildings by Chilean Government standards. The outcomes revealed that climate change impacts did not substantially alter the functional performance of the set of heritage timber buildings examined. This study’s results can assist in developing upcoming strategies or recommendations that can support adaptation policies for administering architectural heritage regarding climate change forecasts. These data will invaluably help stakeholders who support the conservation of timber structures located in the southern environment of Chile and under the changing climatic hazard.

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A Multitasking Genetic Algorithm for Mamdani Fuzzy System with Fully Overlapping Triangle Membership Functions

Cited by 13 publications

References 39 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

An Approach Concerning Climate Change and Timber Building Resilience: Araucanía Region, South Chile

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