# Half a Dozen Real-World Applications of Evolutionary Multitasking, and More

arXiv: https://arxiv.org/abs/2109.13101 · HTML: https://mlsanity.com/paper/2109.13101/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 129 references that are in the map.
- First posted on arXiv 27 Sep 2021.

## Builds on (most cited first, 13 of 13)

- [Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning](https://mlsanity.com/paper/1910.10897/) (2019)
- [Illuminating search spaces by mapping elites](https://mlsanity.com/paper/1504.04909/) (2015)
- [A Survey on Evolutionary Neural Architecture Search](https://mlsanity.com/paper/2008.10937/) (2020)
- [A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications](https://mlsanity.com/paper/1705.06270/) (2017)
- [Quality Diversity for Multi-task Optimization](https://mlsanity.com/paper/2003.04407/) (2020)
- [Transfer Learning based Dynamic Multiobjective Optimization Algorithms](https://mlsanity.com/paper/1612.06093/) (2016)
- [Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks](https://mlsanity.com/paper/1810.06773/) (2018)
- [Learning and Sharing: A Multitask Genetic Programming Approach to Image Feature Learning](https://mlsanity.com/paper/2012.09444/) (2020)
- [Evolutionary Multitask Optimization: a Methodological Overview, Challenges and Future Research Directions](https://mlsanity.com/paper/2102.02558/) (2021)
- [Multitask Evolution with Cartesian Genetic Programming](https://mlsanity.com/paper/1702.02217/) (2017)
- [Addressing Expensive Multi-objective Games with Postponed Preference Articulation via Memetic Co-evolution](https://mlsanity.com/paper/1711.06763/) (2017)
- [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)

## Built on by (most cited first, 12 of 12)

- [Evolutionary Optimization of Physics-Informed Neural Networks: Evo-PINN Frontiers and Opportunities](https://mlsanity.com/paper/2501.06572/) (2025)
- [Evolvable Conditional Diffusion](https://mlsanity.com/paper/2506.13834/) (2025)
- [Parametric-Task MAP-Elites](https://mlsanity.com/paper/2402.01275/) (2024)
- [(θl, θu)-Parametric Multi-Task Optimization: Joint Search in Solution and Infinite Task Spaces](https://mlsanity.com/paper/2503.08394/) (2025)
- [Multitask Neuroevolution for Reinforcement Learning with Long and Short Episodes](https://mlsanity.com/paper/2203.10844/) (2022)
- [Multi-Task Learning with Multi-Task Optimization](https://mlsanity.com/paper/2403.16162/) (2024)
- [Knowledge-aware Evolutionary Graph Neural Architecture Search](https://mlsanity.com/paper/2411.17339/) (2024)
- [Learning Where, What and How to Transfer: A Multi-Role Reinforcement Learning Approach for Evolutionary Multitasking](https://mlsanity.com/paper/2511.15199/) (2025)
- [Evolutionary Computation as Natural Generative AI](https://mlsanity.com/paper/2510.08590/) (2025)
- [Multi-Task Optimization over Networks of Tasks](https://mlsanity.com/paper/2604.21991/) (2026)
- [Towards Efficient Evaluation of Evolutionary Transfer Optimization: Case Studies on Task-Parameterized Applications](https://mlsanity.com/paper/2609.05040/) (2026)
- [Solving Few-Shot Multiobjective Multitask Optimization via Iterative Sequential Transfer](https://mlsanity.com/paper/2609.11228/) (2026)

## Abstract (arXiv)

Until recently, the potential to transfer evolved skills across distinct optimization problem instances (or tasks) was seldom explored in evolutionary computation. 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. Despite it being early days, the idea of EMT has begun to show promise in a range of real-world applications. In the backdrop of recent advances, the contribution of this paper is twofold. First, a review of several application-oriented explorations of EMT in the literature is presented; the works are assimilated into half a dozen broad categories according to their respective application domains. Each of these six categories elaborates fundamental motivations to multitask, and contains a representative experimental study (referred from the literature). Second, a set of recipes is provided showing how problem formulations of general interest, those that cut across different disciplines, could be transformed in the new light of EMT. 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.