# Evolutionary Multitask Optimization: a Methodological Overview, Challenges and Future Research Directions arXiv: https://arxiv.org/abs/2102.02558 ยท HTML: https://mlsanity.com/paper/2102.02558/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 21 of its 173 references that are in the map. - First posted on arXiv 4 Feb 2021. ## Builds on (most cited first, 21 of 21) - [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://mlsanity.com/paper/1703.03864/) (2017) - [MUMBO: MUlti-task Max-value Bayesian Optimization](https://mlsanity.com/paper/2006.12093/) (2020) - [No-regret Algorithms for Multi-task Bayesian Optimization](https://mlsanity.com/paper/2008.08885/) (2020) - [Recent Trends in the Use of Statistical Tests for Comparing Swarm and Evolutionary Computing Algorithms: Practical Guidelines and a Critical Review](https://mlsanity.com/paper/2002.09227/) (2020) - [Evolutionary Multitasking for Single-objective Continuous Optimization: Benchmark Problems, Performance Metric, and Baseline Results](https://mlsanity.com/paper/1706.03470/) (2017) - [Lights and Shadows in Evolutionary Deep Learning: Taxonomy, Critical Methodological Analysis, Cases of Study, Learned Lessons, Recommendations and Challenges](https://mlsanity.com/paper/2008.03620/) (2020) - [Scalable Transfer Evolutionary Optimization: Coping with Big Task Instances](https://mlsanity.com/paper/2012.01830/) (2020) - [Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results](https://mlsanity.com/paper/1706.02766/) (2017) - [A Multifactorial Optimization Paradigm for Linkage Tree Genetic Algorithm](https://mlsanity.com/paper/2005.03090/) (2020) - [Evolutionary Algorithm and Multifactorial Evolutionary Algorithm on Clustered Shortest-Path Tree problem](https://mlsanity.com/paper/2010.09309/) (2020) - [Genetic Transfer or Population Diversification? Deciphering the Secret Ingredients of Evolutionary Multitask Optimization](https://mlsanity.com/paper/1607.05390/) (2016) - [Evolutionary Multitasking for Semantic Web Service Composition](https://mlsanity.com/paper/1902.06370/) (2019) - [On the Transferability of Knowledge among Vehicle Routing Problems by using Cellular Evolutionary Multitasking](https://mlsanity.com/paper/2005.05066/) (2020) - [A Coevolutionary Variable Neighborhood Search Algorithm for Discrete Multitasking (CoVNS): Application to Community Detection over Graphs](https://mlsanity.com/paper/2009.14477/) (2020) - [Multi-factorial Optimization for Large-scale Virtual Machine Placement in Cloud Computing](https://mlsanity.com/paper/2001.06585/) (2020) - [Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial Optimization](https://mlsanity.com/paper/2002.12133/) (2020) - [Multifactorial Cellular Genetic Algorithm (MFCGA): Algorithmic Design, Performance Comparison and Genetic Transferability Analysis](https://mlsanity.com/paper/2003.10768/) (2020) - [COEBA: A Coevolutionary Bat Algorithm for Discrete Evolutionary Multitasking](https://mlsanity.com/paper/2003.11628/) (2020) - [dMFEA-II: An Adaptive Multifactorial Evolutionary Algorithm for Permutation-based Discrete Optimization Problems](https://mlsanity.com/paper/2004.06559/) (2020) - [AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking](https://mlsanity.com/paper/2010.03917/) (2020) - [Multiobjective Multitasking Optimization Based on Decomposition with Dual Neighborhoods](https://mlsanity.com/paper/2101.07548/) (2021) ## Built on by (most cited first, 7 of 7) - [Half a Dozen Real-World Applications of Evolutionary Multitasking, and More](https://mlsanity.com/paper/2109.13101/) (2021) - [A Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization](https://mlsanity.com/paper/2206.11526/) (2022) - [Training Physics-Informed Neural Networks via Multi-Task Optimization for Traffic Density Prediction](https://mlsanity.com/paper/2307.03920/) (2023) - [Multi-Asset Closed-Loop Reservoir Management Using Deep Reinforcement Learning](https://mlsanity.com/paper/2207.10376/) (2022) - [Evolutionary Computation for the Design and Enrichment of General-Purpose Artificial Intelligence Systems: Survey and Prospects](https://mlsanity.com/paper/2407.08745/) (2024) - [On the Transfer of Knowledge in Quantum Algorithms](https://mlsanity.com/paper/2501.14120/) (2025) - [Multi-Task Optimization over Networks of Tasks](https://mlsanity.com/paper/2604.21991/) (2026) ## Abstract (arXiv) 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.