Differential evolution variants for Searching D- and A-optimal designs

TL;DR

This paper introduces differential evolution variants with a repair operation to effectively find D- and A-optimal experimental designs, addressing issues of support point determination and infeasible solutions, and demonstrates superior performance across multiple models.

Contribution

It proposes a novel repair operation for DE variants to improve optimal design search, handling support points and infeasible weights automatically.

Findings

LSHADE outperforms other algorithms in D- and A-optimal design problems.

The repair operation effectively determines support points and fixes infeasible weights.

The approach is validated on 12 statistical models.

Abstract

Optimal experimental design is an essential subfield of statistics that maximizes the chances of experimental success. The D- and A-optimal design is a very challenging problem in the field of optimal design, namely minimizing the determinant and trace of the inverse Fisher information matrix. Due to the flexibility and ease of implementation, traditional evolutionary algorithms (EAs) are applied to deal with a small part of experimental optimization design problems without mathematical derivation and assumption. However, the current EAs remain the issues of determining the support point number, handling the infeasible weight solution, and the insufficient experiment. To address the above issues, this paper investigates differential evolution (DE) variants for finding D- and A-optimal designs on several different statistical models. The repair operation is proposed to automatically determine the support point by combining similar support points with their corresponding weights based on Euclidean distance and deleting the support point with less weight. Furthermore, the repair operation fixes the infeasible weight solution into the feasible weight solution. To enrich our optimal design experiments, we utilize the proposed DE variants to test the D- and A-optimal design problems on 12 statistical models. Compared with other competitor algorithms, simulation experiments show that LSHADE can achieve better performance on the D- and A-optimal design problems.