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Evolutionary Multitask Optimization in Real-World Applications: A Survey
2023
Journal of Artificial Intelligence and Technology
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Then it summarizes the application of evolutionary multitask optimization in different scenarios. ...
This paper will explore the existing evolutionary multitasking theory and improvement scheme in detail. ...
There are many challenges in direct feature selection for high-dimensional data due to the curse of dimensionality. ...
doi:10.37965/jait.2023.0149
fatcat:el5tm2grenex7h32n7nn734vfe
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 ...
Multi-objective Optimization versus Multitask Optimization An insightful reader can immediately relate EM to Multi-objective Optimization (MOO) paradigm which, when approached via evolutionary computation ...
arXiv:2102.02558v2
fatcat:3imaqbxisvaehobb3pyf2dbp7y
Half a Dozen Real-World Applications of Evolutionary Multitasking, and More
[article]
2022
arXiv
pre-print
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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. ...
Our discussions emphasize the many practical use-cases of EMT, and is intended to spark future research towards crafting novel algorithms for real-world deployment. ...
In another application for feature selection, the tendency of stagnation of EAs in high-dimensional feature spaces was lessened by initiating information transfers between artificially generated low-dimensional ...
arXiv:2109.13101v4
fatcat:dz2xepsivnberh2ga7d4xw7xqu
Evolutionary Multi-task Ensemble Learning Model for Hyperspectral Image Classification
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Index Terms-Ensemble learning, evolutionary multitasking, feature subspace, hyperspectral images. ...
In this article, an evolutionary multitask ensemble learning model (EMT_EL) for hyperspectral image classification is designed. ...
Evolutionary Multitask Spectral Feature Subspaces Generation As Fig. 4 Part II shows, the spectral feature subspace of each individual classifier can be optimally selected in designed evolutionary multitask ...
doi:10.1109/jstars.2020.3037353
fatcat:rwk7n7qbbvc7rdgyebpc5fqaxa
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 ...
tasks, thereby potentially accelerating convergence and improving the quality of solutions for multi-task optimization problems. ...
Recently, MFPSO was also used to solve high-dimensional classification [173] . ...
doi:10.3390/math9080864
fatcat:nnkdm4zkwvaxveh5cvblhbk5ve
Evolutionary computation for feature selection and feature construction
2022
Proceedings of the Genetic and Evolutionary Computation Conference Companion
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"Adaptive multi-subswarm optimisation for feature selection on high-dimensional classification." ...
Zhou, "An Evolutionary Multitasking-Based Feature Selection Method for High-Dimensional Classification," in IEEE Transactions on Cybernetics, doi: 10.1109/TCYB.2020.3042243. ...
GP for FC in Clustering: Multi-Tree • Each tree creates a single constructed feature. • Each individual contains t trees, to give t constructed features. ...
doi:10.1145/3520304.3533659
fatcat:crjtgbgtxze73jpzkx4pjoqqna
Evolutionary multitasking in bi-level optimization
2015
Complex & Intelligent Systems
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Evolutionary multitasking has recently emerged as an effective means of facilitating implicit genetic transfer across different optimization tasks, thereby potentially accelerating convergence characteristics ...
multitasking into the search process. ...
tasks via implicit genetic transfer, and thereby leading to considerably accelerated convergence towards high quality solutions. ...
doi:10.1007/s40747-016-0011-y
fatcat:dmhl74pbone7tl3zxz7fmjmgsi
A Survey on Computational Intelligence-based Transfer Learning
[article]
2022
arXiv
pre-print
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This paper studies computational intelligence-based transfer learning techniques and categorizes them into neural network-based, evolutionary algorithm-based, swarm intelligence-based and fuzzy logic-based ...
Their multi-factorial evolutionary algorithm (MFEA) proposed in [10] exploits relationships between optimization tasks via multi-tasking through a cross-domain optimization platform. ...
Although multitasking MFEA results in rapid streamlining of search towards feasible solutions in case of latent synergy existence, multi-tasking performance was the same as a singletasking approach for ...
arXiv:2206.10593v1
fatcat:n4bofmrgs5eidciu6b3p3gcxey
A Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization
[article]
2022
arXiv
pre-print
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for environmental selection, learnable evolutionary generators for reproduction, and learnable evolutionary transfer for sharing or reusing optimization experience between different problem domains). ...
, large-scale search space, time-varying environments, and multitask. ...
In this way, they have a high probability of surviving to the next generation, which will prevent the population from moving towards PF. ...
arXiv:2206.11526v3
fatcat:rp2ordnc2nb6dfzv25tyyde5ka
Evolutionary Computation for Feature Selection and Feature Construction
2023
Proceedings of the Companion Conference on Genetic and Evolutionary Computation
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"Improved Crowding Distance in Multi-objective Optimization for Feature Selection in Classification". ...
This is the first work that aims to use the idea of evolutionary multitasking to handle FS in the high-dimensional classification problems. It provides a new effective way for FS. ...
Feature Selection for Explainable ML
Issues and Challenges
Experiment II In this section, the second set of experiments have been conducted, where the feature selection bias issue is removed. ...
doi:10.1145/3583133.3595050
fatcat:4ozmwuzis5ep7kg6q7ktjy4eli
Evolutionary Multitasking for Single-objective Continuous Optimization: Benchmark Problems, Performance Metric, and Baseline Results
[article]
2017
arXiv
pre-print
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In this report, we suggest nine test problems for multi-task single-objective optimization (MTSOO), each of which consists of two single-objective optimization tasks that need to be solved simultaneously ...
The algorithmic realization of the aforementioned notion is achieved in the MFEA via a selective imitation strategy. ...
In order to demonstrate the significance of the ordinal correlation measure in evolutionary multitasking, we consider the case of two minimization tasks T 1 and T 2 with objective/cost functions f 1 and ...
arXiv:1706.03470v1
fatcat:rbzzlmicdncdnfcqfnjxx2wbea
Multitask Learning Strengthens Adversarial Robustness
[article]
2020
arXiv
pre-print
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IEEE Transactions on Evolutionary Computation 15(4), 444-455 (2011) 46. Sener, O., Koltun, V.: Multi-task learning as multi-objective optimization (2018) 47. ...
Multitask Models Against Multitask Attack High Output Dimensionality as Multitask. Our experiment first studies the effect of a higher number of output dimensions on adversarial robustness. ...
arXiv:2007.07236v2
fatcat:kw2fc5lysrd7bhjwvpuwks6uzi
Table of Contents
2020
2020 IEEE Symposium Series on Computational Intelligence (SSCI)
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Eiben
.......... 2272
ENASA1: Neuroevolution/Neural Architecture Design, Chair: Yanan Sun
Objective Comparison and Selection in Mono-and Multi-Objective Evolutionary Neurocontrollers
Ian Showalter ...
Interpretable Routing Policy: A Two Stage Multi-Objective Genetic Programming Approach with
Feature Selection for Uncertain Capacitated Arc Routing Problem
Shaolin Wang, Yi Mei and Mengjie Zhang
.. ...
doi:10.1109/ssci47803.2020.9308155
fatcat:hyargfnk4vevpnooatlovxm4li
EvoX: A Distributed GPU-accelerated Framework for Scalable Evolutionary Computation
[article]
2024
arXiv
pre-print
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2024 pre-print arXiv:2301.12457v9 fatcat:rr4b7kpas5fxtgspseitfeceiqOther Versions
Building upon this foundation, we have crafted an extensive library comprising a wide spectrum of 50+ EC algorithms for both single- and multi-objective optimization. ...
Inspired by natural evolutionary processes, Evolutionary Computation (EC) has established itself as a cornerstone of Artificial Intelligence. ...
Among the promising directions for future development are evolutionary multitasking [68] and evolutionary transfer optimization [69] . ...
arXiv:2301.12457v10
fatcat:asrbzm6ccneg7av36ioe4hi7ym
Multi-grid cellular genetic algorithm for optimizing variable ordering of ROBDDs
2012
2012 IEEE Congress on Evolutionary Computation
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Two feature functions are used to measure the similarity between chromosomes. The approach considers multiple parallel evolving grids. ...
Man and Haibo He, Feature Selection Based on Sparse Imputation 736, Houtao Deng and George Runger, Feature Selection via Regularized Trees Wednesday, IJCNN, WeN 4-6, 14:40-15:40, SVM and Kernel Methods ...
Multi-Objective Optimization 448, Takeshi Uchitane and Toshiharu Hatanaka, Experimental Study for Multi-Objective PSO with Single Objective Guide Selection 459, Jenn-Long Liu, Yu-Tzu Hsu and Chih-Lung ...
doi:10.1109/cec.2012.6256590
dblp:conf/cec/RotaruB12
fatcat:4ly3nrktw5habc6lf5err7d5py
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