Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial optimization

Martinez, Aritz D.; Osaba, Eneko; Sery, Javier Del; Herrera, Francisco

doi:10.1109/cec48606.2020.9185667

Cited by 12 publications

(7 citation statements)

References 12 publications

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“…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%

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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: Domain Problem Algorithmsmentioning

confidence: 99%

“…During the training process, the intermediate knowledge is identified and shared across all tasks to help their training. Recently, Martinez et al [171] also presented a MTEC framework to simultaneously optimize multiple deep Q learning (DQL) models.…”

Section: Machine Learningmentioning

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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“…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%

“…In [50], authors develop a MFEA embedded with a greedy-based allocation operator for solving large-scale virtual machine placement problem in heterogeneous environment. An additional interesting application of MFEA has been recently proposed in [51], with the main goal of simultaneously evolving concurrent deep reinforcement learning models.…”

Section: Implicit Knowledge Transfer Based Static Solversmentioning

confidence: 99%

“…For instance, it has been widely postulated that Evolutionary Computation and Swarm Intelligence solvers can be used as an scalable replacement for optimization problems related to Deep Neural Networks [124,125,126]. Initial explorations have exposed that EM can be used in multitask reinforcement learning environments to jointly train the neural models and exploit synergies between them [51]. However, other avenues at this crossroads are worth to be explored further, such as neural architecture search, where the joint evolution can serve as a mutual guidance for avoiding regions representing underperforming network configurations; and meta-learning, where the paradigm resides in how to optimize models that can perform well in unseen tasks.…”

Section: New Problems In Evolutionary Multitasking: Multimodality Met...mentioning

confidence: 99%

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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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Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial optimization

Cited by 12 publications

References 12 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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