└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # A Collection of Main Papers on Multitasking Optimization 2 | 3 | - [Multitasking Optimization](#Multitasking-Optimization) 4 | - [Survey](#survey) 5 | - [Multitasking Single-objective Optimization](#Multitasking-Single-objective-Optimization) 6 | - [Multitasking Multi-objective Optimization](#Multitasking-Multi-objective-Optimization) 7 | - [Multitasking Combination Optimization](#Multitasking-Combination-Optimization) 8 | - [Multitasking High-dimensional Optimization](#Multitasking-High-dimensional-Optimization) 9 | - [Multitasking Data-Driven Evolutionary Optimization](#Multitasking-Data-Driven-Evolutionary-Optimization) 10 | - [Multitasking Genetic Programming and Swarm Intelligence](#Multitasking-Genetic-Programming-and-Swarm-Intelligence) 11 | - [Multitasking Optimization in Complex Networks](#Multitasking-Optimization-in-Complex-Networks) 12 | - [Multitasking Optimization in Machine Learning](#Multitasking-Optimization-in-Machine-Learning) 13 | - [Transfer Optimization](#Transfer-Optimization) 14 | - [Theoretical Analysis](#Theoretical-Analysis) 15 | - [Other Applications](#Other-Applications) 16 | - [Datasets](#datasets) 17 | - [Tools](#tools) 18 | 19 | ---------- 20 | 21 | # Survey 22 | 23 | | Paper Title | Venue | Year | Authors | Materials | Comment | 24 | | ------------------------------------------------------------ | --------------------- | ---- | -------------- | ------------------------------------------------------------ | ------- | 25 | | 1. Back to the Roots Multi-X Evolutionary Computation | Cognitive Computation | 2019 | _Gupta et al._ | [[paper](https://link.springer.com/article/10.1007/s12559-018-9620-7)] | | 26 | | 2. Evolutionary Multitask Optimization a Methodological Overview Challenges and Future Research Directions | arXiv | 2021 | _Osaba et al._ | [[paper](https://arxiv.org/pdf/2102.02558.pdf)] | | 27 | | 3. Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years A Brief Review | Mathematics | 2021 | _Xu et al._ | [[paper](https://www.mdpi.com/2227-7390/9/8/864)] | | 28 | | 4. Evolutionary Transfer Optimization A New Frontier in Evolutionary Computation Research | CIM | 2021 | _Tan et al._ | [[paper](https://ieeexplore.ieee.org/document/9321762)] | | 29 | | 5. Half a Dozen Real-World Applications of Evolutionary Multitasking and More | CIM | 2022 | _Gupta et al._ | [[paper](https://ieeexplore.ieee.org/document/9756606)] | | 30 | | 6. A Review on Evolutionary Multi-Task Optimization: Trends and Challenges | TEVC | 2022 | _Wei et al._ | [[paper](https://ieeexplore.ieee.org/document/9665768)] | | 31 | | 7. How to Exploit Optimization Experience? Revisiting Evolutionary Sequential Transfer Optimization: Part A - Benchmark Problems | Techrxiv | 2023 | _Xue et al._ | [[paper](https://www.techrxiv.org/articles/preprint/How_to_Exploit_Optimization_Experience_Revisiting_Evolutionary_Sequential_Transfer_Optimization_Part_A_-_Benchmark_Problems/21694754)] | | 32 | | 8. How to Exploit Optimization Experience? Revisiting Evolutionary Sequential Transfer Optimization: Part B - Algorithm Analysis | Techrxiv | 2023 | _Xue et al._ | [[paper](https://www.techrxiv.org/articles/preprint/How_to_Exploit_Optimization_Experience_Revisiting_Evolutionary_Sequential_Transfer_Optimization_Part_B_-_Algorithm_Analysis/21694832)] | | 33 | 34 | 35 | # Multitasking Single-objective Optimization 36 | 37 | | Paper Title | Venue | Year | Authors | Materials | Comment | 38 | | ------------------------------------------------------------ | ----- | ---- | -------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | 39 | | 1. Multifactorial Evolution Toward Evolutionary Multitasking | TEVC | 2016 | _Gupta et al._ | [[paper](https://ieeexplore.ieee.org/document/7161358)] [[code](http://www.bdsc.site/websites/MTO/index.html)] | [[blog](https://blog.csdn.net/qq_40434430/article/details/102748436?spm=1001.2014.3001.5501)] | 40 | | 2. Evolutionary Multitasking via Explicit autoencoding | TCYB | 2018 | _Feng st al._ | [[paper](https://ieeexplore.ieee.org/document/8401802)] [[code](http://www.bdsc.site/websites/MTO/index.html)] | [[blog](https://blog.csdn.net/qq_40434430/article/details/117172668?spm=1001.2014.3001.5501)] | 41 | | 3. A Group-based Approach to Improve Multifactorial Evolutionary Algorithm | IJCAI | 2018 | _Tang st al._ | [[paper](https://www.ijcai.org/proceedings/2018/538)] | | 42 | | 4. Self-regulated Evolutionary Multi-task Optimization | TEVC | 2019 | _Zheng st al._ | [[paper](https://ieeexplore.ieee.org/document/8666053)] [[code](github.com/lyj1ng/sremto)] | [[blog](https://blog.csdn.net/weixin_42345025/article/details/100670172?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522162736760916780269846394%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fall.%2522%257D&request_id=162736760916780269846394&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~first_rank_v2~rank_v29-7-100670172.first_rank_v2_pc_rank_v29&utm_term=%E8%BF%9B%E5%8C%96%E5%A4%9A%E4%BB%BB%E5%8A%A1&spm=1018.2226.3001.4187)] | 43 | | 5. MFEA-II Multifactorial Evolutionary Algorithm with Online Transfer Parameter Estimation MFEA-II | TEVC | 2019 | _Bali st al._ | [[paper](https://ieeexplore.ieee.org/document/8672822)] [[code](http://www.bdsc.site/websites/MTO/index.html)] | [[blog](https://blog.csdn.net/qq_40434430/article/details/102748981?spm=1001.2014.3001.5501)] | 44 | | 6. An Adaptive Archive-Based Evolutionary Framework for Many-Task Optimization | TETCI | 2019 | _Chen st al._ | [[paper](https://ieeexplore.ieee.org/document/8727933)] [[code](https://jinghuizhong.com/many-task-ea/)] | | 45 | | 7. Evolutionary Manytasking Optimization Based on Symbiosis in Biocoenosis | AAAI | 2019 | _Liaw st al._ | [[paper](https://ojs.aaai.org//index.php/AAAI/article/view/4338)] | [[blog](https://blog.csdn.net/qq_40434430/article/details/117172668?spm=1001.2014.3001.5501)] | 46 | | 8. Regularized Evolutionary Multi-Task Optimization Learning to Inter-Task Transfer in Aligned Subspace | TEVC | 2020 | _Tang st al._ | [[paper](https://ieeexplore.ieee.org/document/9195010)] | | 47 | | 9. Solving Multi-task Optimization Problems with Adaptive Knowledge Transfer via Anomaly Detection | TEVC | 2021 | _Wang st al._ | [[paper](https://ieeexplore.ieee.org/document/9385398)] [[code](https://github.com/xiaofangxd/MTEA-AD)] | | 48 | | 10. Evolutionary Multi-task Optimization with Adaptive Knowledge Transfer | TEVC | 2021 | _Xu st al._ | [[paper](https://ieeexplore.ieee.org/document/9521551)] | | 49 | | 11. Evolutionary Many-task Optimization Based on Multi-source Knowledge Transfer | TEVC | 2021 | _Liang st al._ | [[paper](https://ieeexplore.ieee.org/document/9502920)] | 50 | | 12. Multi-Task Shape Optimization Using a 3D Point Cloud Autoencoder as Unified Representation | TEVC | 2021 | _Rios st al._ | [[paper](https://ieeexplore.ieee.org/document/9446541)] | 51 | | 13. Towards Large-Scale Evolutionary Multi-Tasking: A GPU-Based Paradigm | TEVC | 2021 | _Huang st al._ | [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9530458)] | 52 | | 14. Improving Evolutionary Multitasking Optimization by Leveraging Inter-Task Gene Similarity and Mirror Transformation | CIM | 2021 | _Ma st al._ | [[paper](https://ieeexplore.ieee.org/document/9570735)] | 53 | | 15. A Bi-objective Knowledge Transfer Framework for Evolutionary Many-Task Optimization | TEVC | 2022 | _Jiang st al._ | [[paper](https://ieeexplore.ieee.org/abstract/document/9905754)] | 54 | | 16. An Effective Knowledge Transfer Method Based on Semi-supervised Learning for Evolutionary Optimization | IS | 2022 | _Gao st al._ | [[paper](https://www.sciencedirect.com/science/article/abs/pii/S0020025522010660)] | 55 | | 17. Orthogonal Transfer for Multitask Optimization | TEVC | 2022 | _Wu st al._ | [[paper](https://ieeexplore.ieee.org/document/9737234)] | 56 | | 18. Multi-Task Particle Swarm Optimization With Dynamic Neighbor and Level-Based Inter-Task Learning | TETCI | 2022 | _Tang st al._ | [[paper](https://ieeexplore.ieee.org/document/9343727)] | 57 | | 19. Evolutionary Competitive Multitasking Optimization | TEVC | 2023 | _Li st al._ | [[paper](https://ieeexplore.ieee.org/document/9677000)] | 58 | | 20. Ensemble of Domain Adaptation-Based Knowledge Transfer for Evolutionary Multitasking | TEVC | 2023 | _Lin st al._ | [[paper](https://ieeexplore.ieee.org/document/10076907)] | 59 | | 21. Ensemble Multifactorial Evolution with Biased Skill-Factor Inheritance for Many-task Optimization | TEVC | 2022 | _Binh st al._ | [[paper](https://ieeexplore.ieee.org/document/9982674)] | 60 | | 22. Evolutionary Many-Task Optimization Based on Multisource Knowledge Transfer | TEVC | 2022 | _Liang st al._ | [[paper](https://ieeexplore.ieee.org/document/9502920)] | 61 | | 23. Evolutionary Multitasking Optimization Enhanced by Geodesic Flow Kernel | TETCI | 2023 | _Gao st al._ | [[paper](https://ieeexplore.ieee.org/document/10198367)] | 62 | | 24. Evolutionary Multitask Optimization With Lower Confidence Bound-Based Solution Selection Strategy | TEVC | 2024 | _Wang st al._ | [[paper](https://ieeexplore.ieee.org/document/10379504)] | 63 | | 25. Multitask Evolution Strategy With Knowledge-Guided External Sampling | TEVC | 2023 | _Li st al._ | [[paper](https://ieeexplore.ieee.org/document/10309246)] | 64 | | 26. Distributed Knowledge Transfer for Evolutionary Multitask Multimodal Optimization | TEVC | 2023 | _Gao st al._ | [[paper](https://ieeexplore.ieee.org/document/10171983)] | 65 | | 27. Evolutionary Multitasking via Reinforcement Learning | TETCI | 2023 | _Li st al._ | [[paper](https://ieeexplore.ieee.org/document/10144924)] | 66 | | 28. Federated Many-Task Bayesian Optimization | TEVC | 2023 | _Li st al._ | [[paper](https://ieeexplore.ieee.org/document/10141991)] | 67 | | 29. Block-Level Knowledge Transfer for Evolutionary Multitask Optimization | TCYB | 2023 | _Jiang st al._ | [[paper](https://ieeexplore.ieee.org/document/10130298)] | 68 | 69 | 70 | # Multitasking Multi-objective Optimization 71 | 72 | | Paper Title | Venue | Year | Authors | Materials | Comment | 73 | | ------------------------------------------------------------ | ----- | ---- | -------------- | ------------------------------------------------------------ | ------- | 74 | | 1. Multiobjective Multifactorial Optimization in Evolutionary Multitasking | TCYB | 2017 | _Gupta et al._ | [[paper](https://ieeexplore.ieee.org/document/7464295)] [[code](http://www.bdsc.site/websites/MTO/index.html)] | | 75 | | 2. Evolutionary Multitasking via Explicit autoencoding | TCYB | 2018 | _Feng et al._ | [[paper](https://ieeexplore.ieee.org/document/8401802)] [[code](http://www.bdsc.site/websites/MTO/index.html)] | | 76 | | 3. Multiobjective Multitasking OptimizationBased on Incremental Learning | TEVC | 2020 | _Lin et al._ | [[paper](https://ieeexplore.ieee.org/document/8944273)] | | 77 | | 4. An Effective Knowledge Transfer Approach for Multiobjective Multitasking Optimization | TCYB | 2020 | _Lin st al._ | [[paper](https://ieeexplore.ieee.org/document/9032363)] [[code](http://www.bdsc.site/websites/ETO/ETO.html)] | | 78 | | 5. Evolutionary Multitasking for Multiobjective Optimization With Subspace Alignmentand Adaptive Differential Evolution | TCYB | 2020 | _Liang st al._ | [[paper](https://ieeexplore.ieee.org/document/9123962)] [[code](https://github.com/CIA-SZU/DH)] | | 79 | | 6. Cognizant Multitasking in Multiobjective Multifactorial Evolution MO-MFEA-II | TCYB | 2020 | _Bali et al._ | [[paper](https://ieeexplore.ieee.org/document/9076689)] [[code](http://www.bdsc.site/websites/MTO/index.html)] | | 80 | | 7. Multiobjective Multitasking Optimization Based on Decomposition with Dual Neighborhoods | arXiv | 2021 | _Wang et al._ | [[paper](https://arxiv.org/abs/2101.07548)] | | 81 | | 8. Towards Generalized Resource Allocation on Evolutionary Multitasking for Multi-Objective Optimization | CIM | 2021 | _Wei et al._ | [[paper](https://ieeexplore.ieee.org/document/9570733)] | | 82 | | 9. Evolutionary Multitasking for Multi-objective Optimization Based on Generative Strategies | TEVC | 2022 | _Liang et al._ | [[paper](https://ieeexplore.ieee.org/document/9817402)] | | 83 | | 10. Dynamic Auxiliary Task-Based Evolutionary Multitasking for Constrained Multi-objective Optimization | TEVC | 2022 | _Qiao et al._ | [[paper](https://ieeexplore.ieee.org/document/9774845)] | | 84 | | 11. Multiobjective Multitask Optimization -Neighborhood as a Bridge for Knowledge Transfer | TEVC | 2022 | _Wang et al._ | [[paper](https://ieeexplore.ieee.org/document/9721409)] | | 85 | | 12. A Multi-objective Multitask Optimization Algorithm Using Transfer Rank | TEVC | 2022 | _Chen et al._ | [[paper](https://ieeexplore.ieee.org/document/9697101)] | | 86 | | 13. A Multiform Optimization Framework for Constrained Multiobjective Optimization | TCYB | 2022 | _Jiao et al._ | [[paper](https://ieeexplore.ieee.org/document/9793373)] | | 87 | | 14. Multiobjective Multitasking Optimization With Subspace Distribution Alignment and Decision Variable Transfer | TETCI | 2022 | _Gao et al._ | [[paper](https://ieeexplore.ieee.org/document/9582815)] | | 88 | | 15. Adaptive Auxiliary Task Selection for Multitasking-Assisted Constrained Multi-Objective Optimization [Feature] | CIM | 2023 | _Ming et al._ | [[paper](https://ieeexplore.ieee.org/document/10102380)] | | 89 | | 16. Multiobjective Evolutionary Multitasking With Two-Stage Adaptive Knowledge Transfer Based on Population Distribution | TSMC | 2022 | _Liang et al._ | [[paper](https://ieeexplore.ieee.org/document/9495833)] | | 90 | | 17. Learning Task Relationships in Evolutionary Multitasking for Multiobjective Continuous Optimization | TCYB | 2022 | _Chen et al._ | [[paper](https://ieeexplore.ieee.org/document/9262898)] | | 91 | | 18. A Self-Adaptive Evolutionary Multi-Task Based Constrained Multi-Objective Evolutionary Algorithm | TETCI | 2023 | _Qiao et al._ | [[paper](https://ieeexplore.ieee.org/document/10032050)] | | 92 | | 19. Evolutionary Multitasking for Large-Scale Multiobjective Optimization | TEVC | 2022 | _Liu et al._ | [[paper](https://ieeexplore.ieee.org/document/9755945)] | | 93 | | 20. An Evolutionary Multitasking Optimization Framework for Constrained Multiobjective Optimization Problems | TEVC | 2022 | _Qiao et al._ | [[paper](https://ieeexplore.ieee.org/document/9690609)] | | 94 | | 21. Constrained Multi-objective Optimization via Multitasking and Knowledge Transfer | TEVC | 2022 | _Ming et al._ | [[paper](https://ieeexplore.ieee.org/document/9993796)] | | 95 | | 22. A Multivariation Multifactorial Evolutionary Algorithm for Large-Scale Multiobjective Optimization | TEVC | 2023 | _Feng et al._ | [[paper](https://ieeexplore.ieee.org/document/9569756)] | | 96 | | 23. Multifactorial Evolutionary Algorithm Based on Improved Dynamical Decomposition for Many-Objective Optimization Problems | TEVC | 2022 | _Yi et al._ | [[paper](https://ieeexplore.ieee.org/document/9651543)] | | 97 | | 24. Multiobjective Multitask Optimization With Multiple Knowledge Types and Transfer Adaptation | TEVC | 2024 | _Li et al._ | [[paper](https://ieeexplore.ieee.org/document/10398471)] | | 98 | | 25. A Subspace-Knowledge Transfer Based Dynamic Constrained Multiobjective Evolutionary Algorithm | TETCI | 2023 | _Chen et al._ | [[paper](https://ieeexplore.ieee.org/document/10354491)] | | 99 | | 26. A Multi-Form Evolutionary Search Paradigm for Bi-level Multi-Objective Optimization | TEVC | 2023 | _Feng et al._ | [[paper](https://ieeexplore.ieee.org/document/10318213)] | | 100 | | 27. Dynamic Multiobjective Evolutionary Optimization via Knowledge Transfer and Maintenance | TSMC | 2023 | _Lin et al._ | [[paper](https://ieeexplore.ieee.org/document/10292939)] | | 101 | | 28. Evolutionary Multi-Objective Bayesian Optimization Based on Multisource Online Transfer Learning | TETCI | 2023 | _Li et al._ | [[paper](https://ieeexplore.ieee.org/document/10238804)] | | 102 | | 29. Multiple Tasks for Multiple Objectives: A New Multiobjective Optimization Method via Multitask Optimization | TEVC | 2023 | _Li et al._ | [[paper](https://ieeexplore.ieee.org/document/10178002)] | | 103 | | 30. Multiobjective Multitasking Optimization With Decomposition-Based Transfer Selection | TCYB | 2023 | _Lin et al._ | [[paper](https://ieeexplore.ieee.org/document/10136182)] | | 104 | 105 | 106 | # Multitasking Combination Optimization 107 | 108 | | Paper Title | Venue | Year | Authors | Materials | Comment | 109 | | ------------------------------------------------------------ | ------ | ---- | ------------- | ------------------------------------------------------------ | ------- | 110 | | 1. Evolutionary Multitasking in Permutation-Based Combinatorial Optimization Problems Realization with TSP QAP LOP and JSP | TENCON | 2016 | _Yuan et al._ | [[paper](https://ieeexplore.ieee.org/document/7848632)] | | 111 | | 2. Evolutionary Multitasking in Combinatorial Search Spaces A Case Study in Capacitated Vehicle Routing Problem | SSCI | 2016 | _Zhou et al._ | [[paper](https://ieeexplore.ieee.org/document/7850039)] | | 112 | | 3. Solving Generalized Vehicle Routing Problem With Occasional Drivers via Evolutionary Multitasking | TCYB | 2019 | _Feng st al._ | [[paper](https://ieeexplore.ieee.org/document/8938734)] [[code](http://www.bdsc.site/websites/ETO/ETO.html)] | | 113 | | 4. Explicit Evolutionary Multitasking for Combinatorial Optimization A Case Study on Capacitated Vehicle Routing Problem | TCYB | 2020 | _Feng st al._ | [[paper](https://ieeexplore.ieee.org/document/9023952)] [[code](http://www.bdsc.site/websites/ETO/ETO.html)] | | 114 | | 5. A Unified Framework of Graph-based Evolutionary Multitasking Hyper-heuristic | TEVC | 2021 | _Hao et al._ | [[paper](https://ieeexplore.ieee.org/document/9084121)] | | 115 | | 6. Many-Objective Job-Shop Scheduling: A Multiple Populations for Multiple Objectives-Based Genetic Algorithm Approach | TCYB | 2022 | _Liu et al._ | [[paper](https://ieeexplore.ieee.org/document/9536021)] | 116 | | 7. Transfer Learning Assisted Batch Optimization of Jobs Arriving Dynamically in Manufacturing Cloud | JOMS | 2022 | _Zhou et al._ | [[paper](https://www.sciencedirect.com/science/article/abs/pii/S0278612522001297)] | 117 | | 8. Task Relatedness Based Multitask Genetic Programming for Dynamic Flexible Job Shop Scheduling | TEVC | 2022 | _Zhang et al._ | [[paper](https://ieeexplore.ieee.org/document/9861686)] | 118 | | 9. Knowledge Transfer Genetic Programming with Auxiliary Population for Solving Uncertain Capacitated Arc Routing Problem | TEVC | 2022 | _Ardeh et al._ | [[paper](https://ieeexplore.ieee.org/document/9761253)] | 119 | | 10. Multitask Multiobjective Genetic Programming for Automated Scheduling Heuristic Learning in Dynamic Flexible Job-Shop Scheduling | TCYB | 2022 | _Zhang et al._ | [[paper](https://ieeexplore.ieee.org/document/9868257)] | 120 | | 11. Multitask Genetic Programming-Based Generative Hyperheuristics: A Case Study in Dynamic Scheduling | TCYB | 2022 | _Zhang et al._ | [[paper](https://ieeexplore.ieee.org/document/9382963)] | 121 | | 12. Evolutionary Multitasking for Feature Selection in High-Dimensional Classification via Particle Swarm Optimization | TEVC | 2022 | _Chen et al._ | [[paper](https://ieeexplore.ieee.org/document/9496593)] | 122 | | 13. Multitask Linear Genetic Programming with Shared Individuals and its Application to Dynamic Job Shop Scheduling | TEVC | 2023 | _Huang et al._ | [[paper](https://ieeexplore.ieee.org/document/10090245)] | 123 | | 14. Using an Estimation of Distribution Algorithm to Achieve Multitasking Semantic Web Service Composition | TEVC | 2023 | _Wang et al._ | [[paper](https://ieeexplore.ieee.org/document/9765521)] | 124 | | 15. Multitasking Evolutionary Algorithm Based on Adaptive Seed Transfer for Combinatorial Problem | ASOC | 2023 | _Lv et al._ | [[paper](https://arxiv.org/abs/2308.12647)] | 125 | 126 | 127 | 128 | # Multitasking High-dimensional Optimization 129 | 130 | | Paper Title | Venue | Year | Authors | Materials | Comment | 131 | | ------------------------------------------------------------ | ----- | ---- | ------------- | ------------------------------------------------------- | ------- | 132 | | 1. Large Scale optimization via Evolutionary Multitasking assisted Random Embedding | CEC | 2020 | _Feng et al._ | [[paper](https://ieeexplore.ieee.org/document/9185660)] | | 133 | | 2. A Multi-Variation Multifactorial Evolutionary Algorithm for Large-Scale Multi-Objective Optimization | TEVC | 2021 | _Feng et al._ | [[paper](https://ieeexplore.ieee.org/abstract/document/9569756)] | | 134 | | 3. Evolutionary Multitasking for Large-Scale Multiobjective Optimization | TEVC | 2022 | _Liu et al._ | [[paper](https://ieeexplore.ieee.org/document/9755945)] | | 135 | | 4. An Evolutionary Multitasking Algorithm with Multiple Filtering for High-Dimensional Feature Selection | TEVC | 2023 | _Li et al._ | [[paper](https://ieeexplore.ieee.org/document/10064013)] | | 136 | | 5. Evolutionary Optimization with Simplified Helper Task for High-dimensional Expensive Multiobjective Problems | ACM TELO | 2024 | _Wu et al._ | [[paper](https://dl.acm.org/doi/10.1145/3637065)] | | 137 | | 6. Evolutionary Multitasking With Centralized Learning for Large-Scale Combinatorial Multi-Objective Optimization | TEVC | 2023 | _Huang et al._ | [[paper](https://ieeexplore.ieee.org/document/10278184)] | | 138 | | 7. Surrogate and Autoencoder-Assisted Multitask Particle Swarm Optimization for High-Dimensional Expensive Multimodal Problems | TEVC | 2023 | _Ji et al._ | [[paper](https://ieeexplore.ieee.org/document/10155293)] | | 139 | 140 | 141 | 142 | # Multitasking Data-Driven Evolutionary Optimization 143 | 144 | | Paper Title | Venue | Year | Authors | Materials | Comment | 145 | | ------------------------------------------------------------ | ----- | ---- | ------------- | ------------------------------------------------------------ | ------- | 146 | | 1. Generalized Multi-tasking for Evolutionary Optimization of Expensive Problems | TEVC | 2017 | _Ding et al._ | [[paper](https://ieeexplore.ieee.org/document/8231172)] [[code](http://www.soft-computing.de/DDEO.html)] | | 147 | | 2. Novel Multitask Conditional Neural-Network Surrogate Models for Expensive Optimization | TCYB | 2020 | _Luo et al._ | [[paper](https://ieeexplore.ieee.org/document/9186363)] | | 148 | | 3. Evolutionary Multitasking for Expensive Minimax Optimization in Multiple Scenarios | CIM | 2021 | _Wang et al._ | [[paper](https://ieeexplore.ieee.org/document/9321420)] [[code](http://www.soft-computing.de/DDEO.html)] | | 149 | | 4. Multisurrogate-Assisted Multitasking Particle Swarm Optimization for Expensive Multimodal Problems | TCYB | 2023 | _Ji et al._ | [[paper](https://ieeexplore.ieee.org/document/9615147)] | | 150 | | 5. Investigating the Correlation Amongst the Objective and Constraints in Gaussian Process-Assisted Highly Constrained Expensive Optimization | TEVC | 2022 | _Jiao et al._ | [[paper](https://ieeexplore.ieee.org/document/9580458)] | | 151 | | 6. A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments | TEVC | 2023 | _Li et al._ | [[paper](https://ieeexplore.ieee.org/document/10225543)] | | 152 | | 7. A Surrogate-Assisted Differential Evolution with Knowledge Transfer for Expensive Incremental Optimization Problems | TEVC | 2023 | _Liu et al._ | [[paper](https://ieeexplore.ieee.org/document/10172303)] | | 153 | 154 | 155 | 156 | # Multitasking Genetic Programming and Swarm Intelligence 157 | 158 | | Paper Title | Venue | Year | Authors | Materials | Comment | 159 | | ------------------------------------------------------------ | ----- | ---- | -------------- | ------------------------------------------------------------ | ------- | 160 | | 1. Learning Ensemble of Decision Trees through Multifactorial Genetic Programming | CEC | 2016 | _Wen et al._ | [[paper](https://ieeexplore.ieee.org/abstract/document/7748363)] | | 161 | | 2. Multifactorial Genetic Programming for Symbolic Regression Problems | TSMC | 2018 | _Zhong et al._ | [[paper](https://ieeexplore.ieee.org/abstract/document/8419217)] | | 162 | | 3. Self-Adjusting Multi-Task Particle Swarm Optimization | TEVC | 2021 | _Han et al._ | [[paper](https://ieeexplore.ieee.org/document/9491081)] | | 163 | | 4. Self-adaptive Multi-task Particle Swarm Optimization | arxiv | 2021 | _Zheng et al._ | [[paper](https://arxiv.org/pdf/2110.04473.pdf)] | | 164 | | 5. Multi-Task Particle Swarm Optimization with Dynamic On-Demand Allocation | TEVC | 2022 | _Han et al._ | [[paper](https://ieeexplore.ieee.org/document/9811100)] | | 165 | | 6. Multi-Task Particle Swarm Optimization With Dynamic Neighbor and Level-Based Inter-Task Learning | TETCI | 2022 | _Tang et al._ | [[paper](https://ieeexplore.ieee.org/document/9343727)] | | 166 | | 7. A Multitask Bee Colony Band Selection Algorithm With Variable-Size Clustering for Hyperspectral Images | TEVC | 2022 | _He et al._ | [[paper](https://ieeexplore.ieee.org/document/9733922)] | | 167 | | 8. Learning and Sharing: A Multitask Genetic Programming Approach to Image Feature Learning | TEVC | 2022 | _Bi et al._ | [[paper](https://ieeexplore.ieee.org/document/9484082)] | | 168 | | 10. Multitask Particle Swarm Optimization with Heterogeneous Domain Adaptation | TEVC | 2023 | _Han et al._ | [[paper](https://ieeexplore.ieee.org/document/10075542)] | | 169 | | 11. A Meta-Knowledge Transfer-Based Differential Evolution for Multitask Optimization | TEVC | 2022 | _Li et al._ | [[paper](https://ieeexplore.ieee.org/document/9627943)] | | 170 | | 12. Privacy-Enhanced Multitasking Particle Swarm Optimization based on Homomorphic Encryption | TEVC | 2023 | _Li et al._ | [[paper](https://ieeexplore.ieee.org/document/10263995)] | | 171 | 172 | # Multitasking Optimization in Complex Networks 173 | 174 | | Paper Title | Venue | Year | Authors | Materials | Comment | 175 | | ------------------------------------------------------------ | ----- | ---- | ------------- | ------------------------------------------------------------ | ------- | 176 | | 1. Evolutionary Multitasking Sparse Reconstruction Framework and Case Study | TEVC | 2018 | _Li et al._ | [[paper](https://ieeexplore.ieee.org/document/8540026)] | | 177 | | 2. MUMI Multitask Module Identification for Biological Networks | TEVC | 2020 | _Chen et al._ | [[paper](https://ieeexplore.ieee.org/document/8894074)] [[code]( https://github.com/WeiqiChen/Mumimultitask-module-identification)] | | 178 | | 3. Evolutionary Multitasking Network Reconstruction from Time Series with Online Parameter Estimation | KBS | 2021 | _Shen et al._ | [[paper](https://www.sciencedirect.com/science/article/pii/S0950705121002823)] | | 179 | | 4. Learning Large-Scale Fuzzy Cognitive Maps Using an Evolutionary Many-Task Algorithm | ASOC | 2021 | _Wang et al._ | [[paper](https://www.sciencedirect.com/science/article/pii/S1568494621003641)] | | 180 | | 5. Evolutionary Multitasking Multilayer Network Reconstruction | TCYB | 2021 | _Wang et al._ | [[paper](https://ieeexplore.ieee.org/document/9489377)] [[code](https://github.com/xiaofangxd/EM2MNR)] | | 181 | | 6. Community detection in multiplex networks based on evolutionary multi-task optimization and evolutionary clustering ensemble | TEVC | 2022 | _Lyu et al._ | [[paper](https://ieeexplore.ieee.org/document/9802693)] | | 182 | | 7. A Multi-Transformation Evolutionary Framework for Influence Maximization in Social Networks | CIM | 2023 | _Wang et al._ | [[paper](https://ieeexplore.ieee.org/document/10026148)] | | 183 | | 8. Enhancing the Robustness of Networks Against Multiple Damage Models Using a Multifactorial Evolutionary Algorithm | TSMC | 2023 | _Wang et al._ | [[paper](https://ieeexplore.ieee.org/document/10049190)] | | 184 | 185 | 186 | 187 | # Multitasking Optimization in Machine Learning 188 | 189 | | Paper Title | Venue | Year | Authors | Materials | Comment | 190 | | ------------------------------------------------------------ | ----- | ---- | ----------------- | ------------------------------------------------------------ | ------- | 191 | | 1. Multi-task Bayesian Optimization | NIPS | 2012 | _Swersky et al._ | [[paper](https://papers.nips.cc/paper/2013/file/f33ba15effa5c10e873bf3842afb46a6-Paper.pdf)] | | 192 | | 2. Co-evolutionary Multi-Task Learning for Dynamic Time Series Prediction | ASOC | 2018 | _Chandra et al._ | [[paper](https://www.sciencedirect.com/science/article/pii/S1568494618303168)] [[code](https://github.com/rohitash-chandra/CMTL_dynamictimeseries)] | | 193 | | 3. Adaptive Multi-factorial Evolutionary Optimization for Multi-task Reinforcement Learning | TEVC | 2021 | _Martinez et al._ | [[paper](https://ieeexplore.ieee.org/abstract/document/9439811)] [[code](https://git.code.tecnalia.com/aritz.martinez/a-mfea-rl)] | | 194 | | 4. Can Transfer Neuroevolution Tractably Solve Your Differential Equations | arXiv | 2021 | _Huang et al._ | [[paper](https://arxiv.org/abs/2101.01998)] | | 195 | | 5. Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial Optimization | arXiv | 2020 | _Martinez et al._ | [[paper](https://arxiv.org/abs/2002.12133)] [[code](https://git.code.tecnalia.com/aritz.martinez/dql-mfea)] | | 196 | | 6. Evolutionary Multitasking for Feature Selection in High-dimensional Classification via Particle Swarm Optimisation | TEVC | 2021 | _Chen et al._ | [[paper](https://ieeexplore.ieee.org/document/9496593)] | | 197 | | 7. Evolutionary Machine Learning with Minions: A Case Study in Feature Selection | TEVC | 2021 | _Zhang et al._ | [[paper](https://ieeexplore.ieee.org/document/9493747)] | | 198 | | 8. Learning and Sharing: A Multitask Genetic Programming Approach to Image Feature Learning | TEVC | 2021 | _Bi et al._ | [[paper](https://ieeexplore.ieee.org/document/9484082)] | | 199 | | 9. An Evolutionary Multitasking Method for Multiclass Classification [Research Frontier] | CIM | 2022 | _Cheng et al._ | [[paper](https://ieeexplore.ieee.org/document/9942680)] | | 200 | | 10. Evolutionary Multitasking AUC Optimization [Research Frontier] | CIM | 2022 | _Wang et al._ | [[paper](https://ieeexplore.ieee.org/document/9756594)] | | 201 | | 11. Adaptive Multifactorial Evolutionary Optimization for Multitask Reinforcement Learning | TEVC | 2022 | _Martinez et al._ | [[paper](https://ieeexplore.ieee.org/document/9439811)] | | 202 | | 12. ESSR: Evolving Sparse Sharing Representation for Multi-task Learning | TEVC | 2023 | _Zhang et al._ | [[paper](https://ieeexplore.ieee.org/document/10114675)] | | 203 | | 13. Towards Multi-Objective High-Dimensional Feature Selection via Evolutionary Multitasking | Arxiv | 2024 | _Feng et al._ | [[paper](https://arxiv.org/abs/2401.01563)] | | 204 | | 14. Towards Evolutionary Multi-Task Convolutional Neural Architecture Search | TEVC | 2023 | _Zhou et al._ | [[paper](https://ieeexplore.ieee.org/document/10376463)] | | 205 | | 15. Jack and Masters of all Trades: One-Pass Learning Sets of Model Sets From Large Pre-Trained Models | CIM | 2023 | _Choong et al._ | [[paper](https://ieeexplore.ieee.org/abstract/document/10188456)] | | 206 | | 16. A Fast Evolutionary Knowledge Transfer Search for Multiscale Deep Neural Architecture | TNNLS | 2023 | _Zhang et al._ | [[paper](https://ieeexplore.ieee.org/document/10227743)] | | 207 | | 17. Training Physics-Informed Neural Networks via Multi-Task Optimization for Traffic Density Prediction | Arxiv | 2023 | _Wang et al._ | [[paper](https://arxiv.org/abs/2307.03920)] | | 208 | 209 | 210 | 211 | # Transfer Optimization 212 | 213 | | Paper Title | Venue | Year | Authors | Materials | Comment | 214 | | ------------------------------------------------------------ | ----- | ---- | ---------------- | ------------------------------------------------------------ | ------- | 215 | | 1. Insights on Transfer Optimization Because Experience is the Best Teacher | TETCI | 2018 | _Gupta et al._ | [[paper](https://ieeexplore.ieee.org/document/8114198)] | | 216 | | 2. Curbing Negative Influences Online for Seamless Transfer Evolutionary Optimization | TCYB | 2019 | _Da et al._ | [[paper](https://ieeexplore.ieee.org/document/8447304)] [[code](http://www.bdsc.site/websites/MTO/index.html)] | | 217 | | 3. Warm Starting CMA-ES for Hyperparameter Optimization | AAAI | 2020 | _Nomura et al._ | [[paper](https://arxiv.org/abs/2012.06932)] | | 218 | | 4. Generalizing Transfer Bayesian Optimization to Source-Target Heterogeneity | TASE | 2020 | _Min et al._ | [[paper](https://ieeexplore.ieee.org/document/9180071)] | | 219 | | 5. Scalable Transfer Evolutionary Optimization Coping with Big Task Instances | arXiv | 2020 | _Shakeri et al._ | [[paper](https://arxiv.org/abs/2012.01830)] [[code](https://github.com/erfanMhi/Transfer-Optimization)] | | 220 | | 6. Transfer Stacking from Low-to High-Fidelity A Surrogate-Assisted Bi-Fidelity Evolutionary Algorithm | ASOC | 2020 | _Wang et al._ | [[paper](https://www.sciencedirect.com/science/article/pii/S1568494620302167)] [[code](https://sites.google.com/site/handingwanghomepage/publication)] | | 221 | | 7. Transfer Learning Based Co-surrogate Assisted Evolutionary Bi-objective Optimization for Objectives with Non-uniform Evaluation Times | arxiv | 2021 | _Wang et al._ | [[paper](https://arxiv.org/abs/2108.13339)] | | 222 | | 8. Evolutionary Sequential Transfer Optimization for Objective-Heterogeneous Problems | TEVC | 2022 | _Xue st al._ | [[paper](https://ieeexplore.ieee.org/document/9644585)] | 223 | | 9. Transfer Learning Based Co-Surrogate Assisted Evolutionary Bi-Objective Optimization for Objectives with Non-Uniform Evaluation Times | EC | 2022 | _Wang et al._ | [[paper](https://direct.mit.edu/evco/article-abstract/30/2/221/107906/Transfer-Learning-Based-Co-Surrogate-Assisted?redirectedFrom=fulltext)] | 224 | | 10. ExTrEMO: Transfer Evolutionary Multiobjective Optimization With Proof of Faster Convergence | TEVC | 2024 | _Liu et al._ | [[paper](https://ieeexplore.ieee.org/document/10379505)] | 225 | | 11. Inverse Transfer Multiobjective Optimization | Arxiv | 2023 | _Liu et al._ | [[paper](https://arxiv.org/abs/2312.14713)] | 226 | | 12. Solution Transfer in Evolutionary Optimization: An Empirical Study on Sequential Transfer | TEVC | 2023 | _Xue et al._ | [[paper](https://ieeexplore.ieee.org/document/10342789)] | 227 | | 13. Transfer-Based Particle Swarm Optimization for Large-Scale Dynamic Optimization With Changing Variable Interactions | TEVC | 2023 | _Liu et al._ | [[paper](https://ieeexplore.ieee.org/document/10288590)] | 228 | | 14. A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments | TEVC | 2023 | _Li et al._ | [[paper](https://ieeexplore.ieee.org/document/10225543)] | 229 | | 15. First Complexity Results for Evolutionary Knowledge Transfer | FOGA | 2023 | _Eric O Scott et al._ | [[paper](https://dl.acm.org/doi/abs/10.1145/3594805.3607137)] | 230 | 231 | 232 | # Theoretical Analysis 233 | 234 | | Paper Title | Venue | Year | Authors | Materials | Comment | 235 | | ------------------------------------------------------------ | ----- | ---- | -------------- | ------------------------------------------------------------ | ------- | 236 | | 1. Improve Theoretical Upper Bound of Jumpk Function by Evolutionary Multitasking | HPCCT | 2019 | _Lian et al._ | [[paper](https://dl.acm.org/doi/10.1145/3341069.3342982)] | | 237 | | 2. Analysis on the Efficiency of Multifactorial Evolutionary Algorithms | PPSN | 2020 | _Huang et al._ | [[paper](https://link.springer.com/chapter/10.1007%2F978-3-030-58115-2_44)] | | 238 | | 3. From Multi-Task Gradient Descent to Gradient-Free Evolutionary Multitasking A Proof of Faster Convergence | TCYB | 2021 | _Bai et al._ | [[paper](https://ieeexplore.ieee.org/document/9376716)] | | 239 | 240 | 241 | 242 | # Other Applications 243 | 244 | | Paper Title | Venue | Year | Authors | Materials | Comment | 245 | | ------------------------------------------------------------ | ----------------------------- | ---- | -------------- | ------------------------------------------------------------ | ------- | 246 | | 1. Evolutionary Multitasking In Bi-Level Optimization | Complex & Intelligent Systems | 2015 | _Gupta et al._ | [[paper](https://link.springer.com/article/10.1007/s40747-016-0011-y)] | | 247 | | 2. An Evolutionary Multitasking Algorithm for Cloud Computing Service Composition | World Congress on Services | 2018 | _Bao st al._ | [[paper](https://link.springer.com/chapter/10.1007/978-3-319-94472-2_10)] | | 248 | | 3. Multi-Tasking Genetic Algorithm (MTGA) | TFS | 2019 | _Wu et al._ | [[paper](https://ieeexplore.ieee.org/abstract/document/8967000)] | [[blog](http://blog.sciencenet.cn/blog-3418535-1197956.html)] | 249 | | 4. A Multitasking Electric Power Dispatch Approach With Multi-Objective Multifactorial Optimization Algorithm | Access | 2020 | _Liu et al._ | [[paper](https://ieeexplore.ieee.org/document/9173778)] | | 250 | | 5. Solving Dynamic Multiobjective Problem via Autoencoding Evolutionary Search | TCYB | 2020 | _Liang et al._ | [[paper](https://ieeexplore.ieee.org/document/9210737)] [[code](http://www.bdsc.site/websites/ETO/ETO.html)] | | 251 | | 6. A Multi-Task Bee Colony Band Selection Algorithm with Variable-size Clustering for Hyperspectral Images | TEVC | 2022 | _He et al._ | [[paper](https://ieeexplore.ieee.org/document/9733922)] | | 252 | | 7. Predicting Demands of COVID-19 Prevention and Control Materials via Co-Evolutionary Transfer Learning | TCYB | 2022 | _Song et al._ | [[paper](https://ieeexplore.ieee.org/document/9761800)] | | 253 | | 8. Multi-View Point Cloud Registration Based on Evolutionary Multitasking With Bi-Channel Knowledge Sharing Mechanism | TETCI | 2022 | _Wu et al._ | [[paper](https://ieeexplore.ieee.org/document/9903069)] | | 254 | | 9. Evolutionary Multitasking for Costly Task Offloading in Mobile Edge Computing Networks | TEVC | 2022 | _Yang et al._ | [[paper](https://ieeexplore.ieee.org/document/10065579)] | | 255 | | 10. Multitask Shape Optimization Using a 3-D Point Cloud Autoencoder as Unified Representation | TEVC | 2022 | _Rios et al._ | [[paper](https://ieeexplore.ieee.org/document/9446541)] | | 256 | | 11. Evolutionary Multiform Optimization with Two-stage Bidirectional Knowledge Transfer Strategy for Point Cloud Registration | TEVC | 2022 | _Wu et al._ | [[paper](https://ieeexplore.ieee.org/document/9925083)] | | 257 | | 12. Evolutionary Multitasking Optimization Enhanced by Geodesic Flow Kernel | TETCI | 2023 | _Gao et al._ | [[paper](https://ieeexplore.ieee.org/document/10198367)] | | 258 | | 13. Evolutionary Multitasking With Solution Space Cutting for Point Cloud Registration | TETCI | 2023 | _Wu et al._ | [[paper](https://ieeexplore.ieee.org/document/10180214)] | | 259 | 260 | 261 | 262 | # Datasets 263 | 264 | | Paper Title | Venue | Year | Authors | Materials | Comment | 265 | | ------------------------------------------------------------ | ---------- | --------- | ------------- | ------------------------------------------------------------ | ------- | 266 | | 1. Evolutionary Multitasking for Single-objective Continuous Optimization Benchmark Problems Performance Metric and Baseline Results | arXiv | 2017 | _Da et al._ | [[paper](https://arxiv.org/abs/1706.03470)] [[code](http://www.bdsc.site/websites/MTO/index.html)] | | 267 | | 2. Evolutionary Multitasking for Multiobjective Continuous Optimization Benchmark Problems Performance Metrics and Baseline Results | arXiv | 2017 | _Yuan et al._ | [[paper](https://arxiv.org/abs/1706.02766)] [[code](http://www.bdsc.site/websites/MTO/index.html)] | | 268 | | 3. Evolutionary Multitasking Optimization for Complex Problems | CEC, GECCO | 2017~2021 | _Feng et al._ | [[code](http://www.bdsc.site/websites/MTO_competition_2021/MTO_Competition_CEC_2021.html)] | | 269 | | 4. Evolutionary Many-tasking Optimization | CEC, GECCO | 2017~2021 | _Feng et al._ | [[code](http://www.bdsc.site/websites/MTO_competition_2021/MTO_Competition_CEC_2021.html)] | | 270 | | 5. Evolutionary Transfer Optimization | CEC | 2021 | _Tan et al._ | [[paper](https://arxiv.org/ftp/arxiv/papers/2110/2110.08033.pdf)] [[code](https://www.scholat.com/vpost.html?pid=160180)] | | 271 | | 6. Evolutionary Constrained Multiobjective Optimization: Scalable High-Dimensional Constraint Benchmarks and Algorithm | TEVC | 2023 | _Qiao et al._ | [[paper](https://ieeexplore.ieee.org/document/10139843)] [[code]( https://github.com/cilabzzu/Codes/blob/main/SDC)] | | 272 | 273 | 274 | 275 | # Tools 276 | 277 | | Name | Authors/Organizations | Materials | Comment | 278 | | ----------- | -------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | 279 | | 1. Deap | _Fortin et al._ | [[paper](https://www.jmlr.org/papers/volume13/fortin12a/fortin12a.pdf)] [[code](https://pypi.org/project/deap/)] | DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. It seeks to make algorithms explicit and data structures transparent. It works in perfect harmony with parallelisation mechanisms such as multiprocessing and [SCOOP](https://github.com/soravux/scoop). | 280 | | 2. Geatpy2 | _South China Agricultural University et al._ | [[homepage](http://geatpy.com/)] [[code](https://github.com/geatpy-dev/geatpy)] | Capability of solving single-objective, multi-objectives, many-objectives and combinatorial optimization problems fast. A huge number of operators with high performance of evolutionary algorithms (selection, recombination, mutation, migration...). Support numerous encodings for the chromosome of the population. Many evolutionary algorithm templates, including GA, DE, ES for single/multi-objective(s) evolution. Multiple population evolution. Support polysomy evolution. Parallelization and distribution of evaluations. Testbeds containing most common benchmarks functions. Support tracking analysis of the evolution iteration. Many evaluation metrics of algorithms. | 281 | | 3. Inspyred | _Garrett et al._ | [[homepage](http://aarongarrett.github.io/inspyred)] [[code](https://github.com/aarongarrett/inspyred)] | Inspyred is a free, open source framework for creating biologically-inspired computational intelligence algorithms in Python, including evolutionary computation, swarm intelligence, and immunocomputing. Additionally, inspyred provides easy-to-use canonical versions of many bio-inspired algorithms for users who do not need much customization. | 282 | | 4. PlatEMO | _Tian et al._ | [[paper](https://ieeexplore.ieee.org/document/8065138)] [[code](https://github.com/BIMK/PlatEMO)] | Developed by BIMK (Institute of Bioinspired Intelligence and Mining Knowledge) of Anhui University and NICE (Nature Inspired Computing and Engineering Group) of University of Surrey. 150+ open source evolutionary algorithms, 300+ open source benchmark problems, Powerful GUI for performing experiments in parallel, Generating results in the format of Excel or LaTeX table by one-click operation, State-of-the-art algorithms will be included continuously. | 283 | | 5. EvoGrad | _Uber AI Labs_ | [[code](https://github.com/uber-research/EvoGrad)] | EvoGrad is a lightweight tool for differentiating through expectation, built on top of PyTorch. EvoGrad enables fast prototyping of NES-like algorithms. We believe there are many interesting algorithms yet to be discovered in this vein, and we hope this library will help to catalyze progress in the machine learning community. | 284 | | 6. MToP | _Li et al._ | [[paper](https://arxiv.org/abs/2312.08134)] [[code](https://github.com/intLyc/MTO-Platform)] | MToP provides a user-friendly GUI, enriched algorithms and problems, and convenient code patterns. The current version of MToP includes more than 30 MTEAs, more than 30 single-task EAs (that can handle MTO problems), more than 150 MTO benchmark problems, and several real-world applications of EMT. | 285 | 286 | 287 | **Disclaimer** 288 | 289 | If you have any questions, please feel free to contact us. 290 | Emails: xiaofengxd@126.com 291 | 292 | Authors of scientific papers including results generated using MTEA-AD or EM2MNR are encouraged to cite the following paper: 293 | 294 | @ARTICLE{9489377, author={Wu, Kai and Wang, Chao and Liu, Jing}, journal={IEEE Transactions on Cybernetics}, title={Evolutionary Multitasking Multilayer Network Reconstruction}, year={2022}, volume={52}, number={12}, pages={12854-12868}, doi={10.1109/TCYB.2021.3090769}} 295 | 296 | @ARTICLE{9385398, author={Wang, Chao and Liu, Jing and Wu, Kai and Wu, Zhaoyang}, journal={IEEE Transactions on Evolutionary Computation}, title={Solving Multitask Optimization Problems With Adaptive Knowledge Transfer via Anomaly Detection}, year={2022}, volume={26}, number={2}, pages={304-318}, doi={10.1109/TEVC.2021.3068157}} 297 | 298 | @article{WANG2021107441, 299 | title = {Learning large-scale fuzzy cognitive maps using an evolutionary many-task algorithm}, 300 | journal = {Applied Soft Computing}, 301 | volume = {108}, 302 | pages = {107441}, 303 | year = {2021}, 304 | issn = {1568-4946}, 305 | doi = {https://doi.org/10.1016/j.asoc.2021.107441}, 306 | url = {https://www.sciencedirect.com/science/article/pii/S1568494621003641}, 307 | author = {Chao Wang and Jing Liu and Kai Wu and Chaolong Ying}} 308 | 309 | --------------------------------------------------------------------------------