An estimation of distribution algorithm with adaptive Gibbs sampling for unconstrained global optimization

TL;DR

This paper introduces AGEDA, a new heuristic for unconstrained global optimization that combines adaptive Gibbs sampling with local search within an Estimation of Distribution Algorithm framework, demonstrating robustness across complex problems.

Contribution

The paper presents AGEDA, an innovative EDA that uses adaptive Gibbs sampling and local search, improving solution quality in high-dimensional, multi-modal, and non-smooth problems.

Findings

AGEDA outperforms deterministic and stochastic methods in test problems.

The method is robust against high dimensionality, multi-modality, and non-smoothness.

Experimental results validate the effectiveness of adaptive Gibbs sampling in EDAs.

Abstract

In this paper is proposed a new heuristic approach belonging to the field of evolutionary Estimation of Distribution Algorithms (EDAs). EDAs builds a probability model and a set of solutions is sampled from the model which characterizes the distribution of such solutions. The main framework of the proposed method is an estimation of distribution algorithm, in which an adaptive Gibbs sampling is used to generate new promising solutions and, in combination with a local search strategy, it improves the individual solutions produced in each iteration. The Estimation of Distribution Algorithm with Adaptive Gibbs Sampling we are proposing in this paper is called AGEDA. We experimentally evaluate and compare this algorithm against two deterministic procedures and several stochastic methods in three well known test problems for unconstrained global optimization. It is empirically shown that our heuristic is robust in problems that involve three central aspects that mainly determine the difficulty of global optimization problems, namely high-dimensionality, multi-modality and non-smoothness.