# Evolutionary Dynamic Optimization and Machine Learning

arXiv: https://arxiv.org/abs/2310.08748 · HTML: https://mlsanity.com/paper/2310.08748/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 67 references that are in the map.
- First posted on arXiv 12 Oct 2023.

## Builds on (most cited first, 7 of 7)

- [A Survey on Evolutionary Neural Architecture Search](https://mlsanity.com/paper/2008.10937/) (2020)
- [Transfer Learning based Dynamic Multiobjective Optimization Algorithms](https://mlsanity.com/paper/1612.06093/) (2016)
- [Solving Dynamic Multi-objective Optimization Problems Using Incremental Support Vector Machine](https://mlsanity.com/paper/1910.08751/) (2019)
- [An Online Prediction Approach Based on Incremental Support Vector Machine for Dynamic Multiobjective Optimization](https://mlsanity.com/paper/2102.12133/) (2021)
- [Solving dynamic multi-objective optimization problems via support vector machine](https://mlsanity.com/paper/1910.08747/) (2019)
- [Evolutionary Dynamic Multi-objective Optimization Via Regression Transfer Learning](https://mlsanity.com/paper/1910.08753/) (2019)
- [Using Neural Networks and Diversifying Differential Evolution for Dynamic Optimisation](https://mlsanity.com/paper/2008.04002/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Unified Framework for Combinatorial Optimization Based on Graph Neural Networks](https://mlsanity.com/paper/2406.13125/) (2024)

## Abstract (arXiv)

Evolutionary Computation (EC) has emerged as a powerful field of Artificial Intelligence, inspired by nature's mechanisms of gradual development. However, EC approaches often face challenges such as stagnation, diversity loss, computational complexity, population initialization, and premature convergence. To overcome these limitations, researchers have integrated learning algorithms with evolutionary techniques. This integration harnesses the valuable data generated by EC algorithms during iterative searches, providing insights into the search space and population dynamics. Similarly, the relationship between evolutionary algorithms and Machine Learning (ML) is reciprocal, as EC methods offer exceptional opportunities for optimizing complex ML tasks characterized by noisy, inaccurate, and dynamic objective functions. These hybrid techniques, known as Evolutionary Machine Learning (EML), have been applied at various stages of the ML process. EC techniques play a vital role in tasks such as data balancing, feature selection, and model training optimization. Moreover, ML tasks often require dynamic optimization, for which Evolutionary Dynamic Optimization (EDO) is valuable. This paper presents the first comprehensive exploration of reciprocal integration between EDO and ML. The study aims to stimulate interest in the evolutionary learning community and inspire innovative contributions in this domain.