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Evolutionary Multitask Optimization: a Methodological Overview, Challenges and Future Research Directions
[article]
2021
arXiv
pre-print
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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 ...
Furthermore, similar authors that proposed MFEA-II in 2019, introduced two years before a Linearized Domain Adaptation MFEA (LDA-MFEA) [21] . ...
arXiv:2102.02558v2
fatcat:3imaqbxisvaehobb3pyf2dbp7y
CIS Publication Spotlight [Publication Spotlight]
2020
IEEE Computational Intelligence Magazine
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Evolutionary multitasking, or multifactorial optimization, is an emerging subfield of multitask optimi zation, which integrates evolutionary computation and multitask learning. ...
In order to design a CNN with promising performance, extensive expertise in both CNNs and the investigated problem domain is required, which is not necessarily avail able to every interested user. ...
doi:10.1109/mci.2020.3019871
fatcat:icdr7pp7grbqhoxlzjlwps2ro4
muNet: Evolving Pretrained Deep Neural Networks into Scalable Auto-tuning Multitask Systems
[article]
2022
arXiv
pre-print
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We define an evolutionary approach designed to jointly select the prior knowledge relevant for each task, choose the subset of the model parameters to train and dynamically auto-tune its hyperparameters ...
The resulting system can leverage cross tasks knowledge transfer, while being immune from common drawbacks of multitask approaches such as catastrophic forgetting, gradients interference and negative transfer ...
While, an increase in compute leads to a linear increase in exploration budget. ...
arXiv:2205.10937v2
fatcat:qy3wo5pvwnadzlz3vqapgzt7ne
Evolutionary Architecture Search For Deep Multitask Networks
[article]
2018
arXiv
pre-print
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The size and complexity of this problem exceeds human design ability, making it a compelling domain for evolutionary optimization. ...
character recognition domain. ...
Evolutionary methods have also had success in MTL, especially in sequential decision-making domains [ Figure 1 : Example soft ordering network with three shared layers. ...
arXiv:1803.03745v2
fatcat:pcjt222zbfesrlpiwwxd2go3qq
Learning and Sharing: A Multitask Genetic Programming Approach to Image Feature Learning
[article]
2020
arXiv
pre-print
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Experimental results show that the new approach outperforms these compared methods in almost all the comparisons. ...
Using evolutionary computation algorithms to solve multiple tasks with knowledge sharing is a promising approach. ...
The adaptive knowledge transfer is achieved by changing the crossover operators in MEFA using the information collected from the evolutionary process. ...
arXiv:2012.09444v2
fatcat:hkcij2mltnfa5cn2qzvyndfqpa
Table of contents
2021
IEEE Transactions on Cybernetics
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Kwong 2550 Toward Adaptive Knowledge Transfer in Multifactorial Evolutionary Computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
Yan 2663 Joint Learning of Multiple Latent Domains and Deep Representations for Domain Adaptation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
doi:10.1109/tcyb.2021.3071166
fatcat:2rhnoufzxbeh3k7xhwyenvwwvu
AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking
[article]
2021
arXiv
pre-print
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In this paper we introduce a novel adaptive metaheuristic algorithm to deal with Evolutionary Multitasking environments coined as Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm (AT-MFCGA ...
Among the different approaches that can address this problem effectively, Evolutionary Multitasking resorts to concepts from Evolutionary Computation to solve multiple problems within a single search process ...
In [48] , a solver referred to as self-regulated evolutionary multitasking optimization is presented. ...
arXiv:2010.03917v2
fatcat:vgp6vsjpzreb7hwxqxbtnnlfmi
Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief Review
2021
Mathematics
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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 ...
in this fascinating territory. ...
[20] , multitask optimization (MTO) [11] , multitasking [12] , evolutionary multitasking (EMT) [21] , evolutionary multitasking (EMT) [22] , and multifactorial operation optimization (MFOO) [23] . ...
doi:10.3390/math9080864
fatcat:nnkdm4zkwvaxveh5cvblhbk5ve
An Effective Variable Transformation Strategy in Multitasking Evolutionary Algorithms
2020
Complexity
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Multitasking evolutionary algorithm (MTEA), which solves multiple optimization tasks simultaneously in a single run, has received considerable attention in the community of evolutionary computation, and ...
Keeping this in mind, a multitasking evolutionary algorithm (named MTDE-VT) is realized as an instance by embedding the proposed variable transformation strategy into multitasking differential evolution ...
Few works have been conducted in the literature for variable transformation in evolutionary multitasking. ...
doi:10.1155/2020/8815117
fatcat:losbzans5rfylectvjgjppbt2m
Leveraging Sequence Classification by Taxonomy-Based Multitask Learning
[chapter]
2010
Lecture Notes in Computer Science
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We treat each organism as one task and present three novel multitask learning methods to handle situations in which the relationships among tasks can be described by a hierarchy or by a graph. ...
Multitask learning, a machine learning technique that recently received considerable attention, considers the problem of learning across tasks that are related to each other. ...
Acknowledgments We would like to thank Sören Sonnenburg for help with the implementation of algorithms presented in this work. ...
doi:10.1007/978-3-642-12683-3_34
fatcat:6bko4pwfobb4fo4bte46gu64ka
A Survey of Transfer and Multitask Learning in Bioinformatics
2011
Journal of Computing Science and Engineering
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Transfer and multitask learning offer an attractive alternative, by allowing useful knowledge to be extracted and transferred from data in auxiliary domains helps counter the lack of data problem in the ...
In this article, we survey recent advances in transfer and multitask learning for bioinformatics applications. ...
Multitask learning stresses generating benefits for learning performance improvements in all related domains. ...
doi:10.5626/jcse.2011.5.3.257
fatcat:oruxwutza5godismndbqad2lm4
A Two-Level Transfer Learning Algorithm for Evolutionary Multitasking
2020
Frontiers in Neuroscience
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The well-known multifactorial evolutionary algorithm (MFEA) has been successfully introduced to solve MTO problems based on transfer learning. ...
However, it uses a simple and random inter-task transfer learning strategy, thereby resulting in slow convergence. ...
Bali et al. (2017) put forward a linearized domain adaptation strategy to deal with the issue of the negative knowledge transfer between uncorrelated tasks. ...
doi:10.3389/fnins.2019.01408
pmid:31992969
pmcid:PMC6971124
fatcat:hp3fwedowba3thjmx3t3yxxuza
Inferring latent task structure for Multitask Learning by Multiple Kernel Learning
2010
BMC Bioinformatics
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If data is available for several different but related problem domains, Multitask Learning algorithms can be used to learn a model based on all available information. ...
In Bioinformatics, many problems can be cast into the Multitask Learning scenario by incorporating data from several organisms. ...
There has been previous work using Domain Adaptation (closely related to Multitask Learning) in the context of splice site prediction [3] . ...
doi:10.1186/1471-2105-11-s8-s5
pmid:21034430
pmcid:PMC2966292
fatcat:43h5vn5bizafldf6irpzicx6fi
Evolutionary Neural AutoML for Deep Learning
[article]
2019
arXiv
pre-print
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Deep neural networks (DNNs) have produced state-of-the-art results in many benchmarks and problem domains. ...
LEAF makes use of both state-of-the-art evolutionary algorithms (EAs) and distributed computing frameworks. ...
ACKNOWLEDGEMENTS Thanks to Joseph Sirosh and Justin Ormont for inspiring discussions and feedback in the course of this research, and in particular to Joseph for suggestions on minimization and hyperparameter ...
arXiv:1902.06827v3
fatcat:aojhwsjd5ner7c43hojv5isj3i
Half a Dozen Real-World Applications of Evolutionary Multitasking, and More
[article]
2022
arXiv
pre-print
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domains. ...
The concept of evolutionary multitasking (EMT) fills this gap. It unlocks a population's implicit parallelism to jointly solve a set of tasks, hence creating avenues for skills transfer between them. ...
CONCLUSIONS Evolutionary multitasking (EMT) is an emerging paradigm for jointly solving multiple tasks in a single optimization run. ...
arXiv:2109.13101v4
fatcat:dz2xepsivnberh2ga7d4xw7xqu
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