Accelerating the Evolutionary Algorithms by Gaussian Process Regression with $\epsilon$-greedy acquisition function

TL;DR

This paper introduces a method combining Gaussian Process Regression with an $$-greedy acquisition function to estimate elite individuals, thereby accelerating convergence in various evolutionary algorithms like GA, DE, and CMA-ES.

Contribution

It proposes a novel approach using GPR and an $$-greedy strategy to improve elite individual estimation and convergence speed in evolutionary algorithms.

Findings

Accelerates convergence in benchmark functions

Effective across multiple evolutionary algorithms

Broad applicability demonstrated

Abstract

In this paper, we propose a novel method to estimate the elite individual to accelerate the convergence of optimization. Inspired by the Bayesian Optimization Algorithm (BOA), the Gaussian Process Regression (GPR) is applied to approximate the fitness landscape of original problems based on every generation of optimization. And simple but efficient $\epsilon$-greedy acquisition function is employed to find a promising solution in the surrogate model. Proximity Optimal Principle (POP) states that well-performed solutions have a similar structure, and there is a high probability of better solutions existing around the elite individual. Based on this hypothesis, in each generation of optimization, we replace the worst individual in Evolutionary Algorithms (EAs) with the elite individual to participate in the evolution process. To illustrate the scalability of our proposal, we combine our proposal with the Genetic Algorithm (GA), Differential Evolution (DE), and CMA-ES. Experimental results in CEC2013 benchmark functions show our proposal has a broad prospect to estimate the elite individual and accelerate the convergence of optimization.