An Ensemble of Evolutionary Algorithms With Both Crisscross Search and Sparrow Search for Processing Inferior Individuals

TL;DR

This paper introduces EA4eigCS, an ensemble of evolutionary algorithms combining crisscross search and sparrow search strategies to enhance long-term optimization performance by processing inferior individuals.

Contribution

The paper proposes integrating recent search strategies into an existing ensemble to improve exploration and avoid stagnation in evolutionary algorithms.

Findings

EA4eigCS outperforms EA4eig in experiments.

EA4eigCS is competitive with state-of-the-art algorithms.

The new ensemble effectively processes inferior individuals to enhance optimization.

Abstract

In the field of artificial intelligence, real parameter single objective optimization is an important direction. Both the Differential Evolution (DE) and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) demonstrate good performance for real parameter single objective optimization. Nevertheless, there exist other types of evolutionary algorithm for the purpose. In recent years, researchers begin to study long-term search. EA4eig - an ensemble of three DE variants and CMA-ES - performs well for long-term search. In this paper, we introduce two types of evolutionary algorithm proposed recently - crisscross search and sparrow search - into EA4eig as secondary evolutionary algorithms to process inferior individuals. Thus, EA4eigCS is obtained. In our ensemble, the secondary evolutionary algorithms are expected to vary distribution of the population for breaking stagnation. Experimental results show that our EA4eigCS outperforms EA4eig and is competitive when compared with state-of-the-art algorithms. Code and supplementary material are available at:https://anonymous.4open.science/r/EA4eigCS-2A43.