# A Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization

arXiv: https://arxiv.org/abs/2206.11526 · HTML: https://mlsanity.com/paper/2206.11526/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 319 references that are in the map.
- First posted on arXiv 23 Jun 2022.

## Builds on (most cited first, 28 of 28)

- [A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications](https://mlsanity.com/paper/1705.06270/) (2017)
- [Deep Reinforcement Learning for Multi-objective Optimization](https://mlsanity.com/paper/1906.02386/) (2019)
- [Evolutionary Generative Adversarial Networks](https://mlsanity.com/paper/1803.00657/) (2018)
- [Benchmarking in Optimization: Best Practice and Open Issues](https://mlsanity.com/paper/2007.03488/) (2020)
- [Learning adaptive differential evolution algorithm from optimization experiences by policy gradient](https://mlsanity.com/paper/2102.03572/) (2021)
- [An Overview and Experimental Study of Learning-based Optimization Algorithms for Vehicle Routing Problem](https://mlsanity.com/paper/2107.07076/) (2021)
- [Multiobjective optimization using Gaussian process emulators via stepwise uncertainty reduction](https://mlsanity.com/paper/1310.0732/) (2013)
- [Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization Problems](https://mlsanity.com/paper/2105.02741/) (2021)
- [Transfer Learning based Dynamic Multiobjective Optimization Algorithms](https://mlsanity.com/paper/1612.06093/) (2016)
- [Multi-Objective Learning to Predict Pareto Fronts Using Hypervolume Maximization](https://mlsanity.com/paper/2102.04523/) (2021)
- [Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)](https://mlsanity.com/paper/1910.04966/) (2019)
- [MODRL/D-EL: Multiobjective Deep Reinforcement Learning with Evolutionary Learning for Multiobjective Optimization](https://mlsanity.com/paper/2107.07961/) (2021)
- [Manifold Interpolation for Large-Scale Multi-Objective Optimization via Generative Adversarial Networks](https://mlsanity.com/paper/2101.02932/) (2021)
- [Evolutionary Multitask Optimization: a Methodological Overview, Challenges and Future Research Directions](https://mlsanity.com/paper/2102.02558/) (2021)
- [Scalable Transfer Evolutionary Optimization: Coping with Big Task Instances](https://mlsanity.com/paper/2012.01830/) (2020)
- [Transfer Learning Based Co-surrogate Assisted Evolutionary Bi-objective Optimization for Objectives with Non-uniform Evaluation Times](https://mlsanity.com/paper/2108.13339/) (2021)
- [A Review of Evolutionary Multi-modal Multi-objective Optimization](https://mlsanity.com/paper/2009.13347/) (2020)
- [Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for Metaheuristics](https://mlsanity.com/paper/2105.10657/) (2021)
- [Decomposition Multi-Objective Evolutionary Optimization: From State-of-the-Art to Future Opportunities](https://mlsanity.com/paper/2108.09588/) (2021)
- [Hybridization of evolutionary algorithm and deep reinforcement learning for multi-objective orienteering optimization](https://mlsanity.com/paper/2206.10464/) (2022)
- [A Two stage Adaptive Knowledge Transfer Evolutionary Multi-tasking Based on Population Distribution for Multi/Many-Objective Optimization](https://mlsanity.com/paper/2001.00810/) (2020)
- [Batched Data-Driven Evolutionary Multi-Objective Optimization Based on Manifold Interpolation](https://mlsanity.com/paper/2109.05639/) (2021)
- [Self-Evolutionary Optimization for Pareto Front Learning](https://mlsanity.com/paper/2110.03461/) (2021)
- [Evolutionary Multitask Optimization: Fundamental Research Questions, Practices, and Directions for the Future](https://mlsanity.com/paper/2111.14463/) (2021)
- [Preselection via Classification: A Case Study on Evolutionary Multiobjective Optimization](https://mlsanity.com/paper/1708.01146/) (2017)
- [Improved Regularity Model-based EDA for Many-objective Optimization](https://mlsanity.com/paper/1802.08788/) (2018)
- [An Online Prediction Approach Based on Incremental Support Vector Machine for Dynamic Multiobjective Optimization](https://mlsanity.com/paper/2102.12133/) (2021)
- [A Many-Objective Evolutionary Algorithm With Two Interacting Processes: Cascade Clustering and Reference Point Incremental Learning](https://mlsanity.com/paper/1803.01097/) (2018)

## Built on by (most cited first, 7 of 7)

- [Large Language Model for Multi-objective Evolutionary Optimization](https://mlsanity.com/paper/2310.12541/) (2023)
- [HM3: Hierarchical Multi-Objective Model Merging for Pretrained Models](https://mlsanity.com/paper/2409.18893/) (2024)
- [LLM See, LLM Do: Guiding Data Generation to Target Non-Differentiable Objectives](https://mlsanity.com/paper/2407.01490/) (2024)
- [Diffusion Model-Based Multiobjective Optimization for Gasoline Blending Scheduling](https://mlsanity.com/paper/2402.14600/) (2024)
- [Rank-Based Learning and Local Model Based Evolutionary Algorithm for High-Dimensional Expensive Multi-Objective Problems](https://mlsanity.com/paper/2304.09444/) (2023)
- [Benchmark for CEC 2024 Competition on Multiparty Multiobjective Optimization](https://mlsanity.com/paper/2402.02033/) (2024)
- [ParetoLens: A Visual Analytics Framework for Exploring Solution Sets of Multi-objective Evolutionary Algorithms](https://mlsanity.com/paper/2501.02857/) (2025)

## Abstract (arXiv)

Recent decades have witnessed great advancements in multiobjective evolutionary algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these progressively improved MOEAs have not necessarily been equipped with scalable and learnable problem-solving strategies for new and grand challenges brought by the scaling-up MOPs with continuously increasing complexity from diverse aspects, mainly including expensive cost of function evaluations, many objectives, large-scale search space, time-varying environments, and multi-task. Under different scenarios, divergent thinking is required in designing new powerful MOEAs for solving them effectively. In this context, research studies on learnable MOEAs with machine learning techniques have received extensive attention in the field of evolutionary computation. This paper begins with a general taxonomy of scaling-up MOPs and learnable MOEAs, followed by an analysis of the challenges that these MOPs pose to traditional MOEAs. Then, we synthetically overview recent advances of learnable MOEAs in solving various scaling-up MOPs, focusing primarily on four attractive directions (i.e., learnable evolutionary discriminators for environmental selection, learnable evolutionary generators for reproduction, learnable evolutionary evaluators for function evaluations, and learnable evolutionary transfer modules for sharing or reusing optimization experience). The insight of learnable MOEAs is offered to readers as a reference to the general track of the efforts in this field.