# An Experimental Study of Adaptive Control for Evolutionary Algorithms

arXiv: https://arxiv.org/abs/1409.1715 · HTML: https://mlsanity.com/paper/1409.1715/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2014.

## Built on by (most cited first, 2 of 2)

- [RefineEvo: Planning-Guided Heuristic Evolution with Bidirectional Experience](https://mlsanity.com/paper/2607.11358/) (2026)
- [Dynamic operator management in meta-heuristics using reinforcement learning: an application to permutation flowshop scheduling problems](https://mlsanity.com/paper/2408.14864/) (2024)

## Abstract (arXiv)

The balance of exploration versus exploitation (EvE) is a key issue on evolutionary computation. In this paper we will investigate how an adaptive controller aimed to perform Operator Selection can be used to dynamically manage the EvE balance required by the search, showing that the search strategies determined by this control paradigm lead to an improvement of solution quality found by the evolutionary algorithm.