An Experimental Study of Adaptive Control for Evolutionary Algorithms

TL;DR

This paper investigates an adaptive control method for operator selection in evolutionary algorithms to dynamically balance exploration and exploitation, leading to improved solution quality.

Contribution

It introduces an adaptive control approach for operator selection that effectively manages the exploration-exploitation trade-off in evolutionary algorithms.

Findings

Adaptive control improves solution quality

Dynamic operator selection enhances search efficiency

Method outperforms static strategies

Abstract

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.