Using Genetic Algorithms to Design Experiments: A Review

Abstract

Genetic algorithms (GAs) have been used in many disciplines to optimize solutions for a broad range of problems. In the last 20 years, the statistical literature has seen an increase in the use and study of this optimization algorithm for generating optimal designs in a diverse set of experimental settings. These efforts are due in part to an interest in implementing a novel methodology as well as the hope that careful application of elements of the GA framework to the unique aspects of a designed experiment problem might lead to an efficient means of finding improved or optimal designs. In this paper, we explore the merits of using this approach, some of the aspects of design that make it a unique application relative to other optimization scenarios, and discuss elements which should be considered for an effective implementation. We conclude that the current GA implementations can, but do not always, provide a competitive methodology to produce substantial gains over standard optimal design strategies. We consider both the probability of finding a globally optimal design as well as the computational efficiency of this approach. Copyright © 2014 John Wiley & Sons, Ltd.

Exact optimal experimental designs with constraints

“…Instead of exploring the solution space systematically, heuristic algorithms define criteria (heuristic) that drive the search towards promising regions of the solution space. Some state-of-the-art heuristic algorithms are available in the optimal design literature such as genetic algorithms (Mandal et al 2006;Lin et al 2015), simulated annealing (Woods 2010) and Particle Swarm algorithms (Chen et al 2014). For details, see Mandal et al (2015).…”

Multi-Objective Optimization of Wavelet-Packet-Based Features in Pathological Diagnosis of Alzheimer Using Spontaneous Speech Signals

“…The meta-heuristic algorithms inspired by the natural process of evolution, have been widely applied for finding the global optima in complex search spaces [42]- [44]. These algorithms require an objective function to perform the search according to the problem under study.…”

Optimal parameter identification of synthetic gene networks using harmony search algorithm

“…In order to compare the proposed HS-based OED with other approaches, various optimization are compared in the inner iteration loop, where the fitness function denote estimation error. Heuristic optimization approaches including particle swarm optimization(PSO) and genetic algorithm(GA) are able to perform the task of optimal experimental design with suitable fitness functions [41, 42]. Under the framework of deterministic modeling, GA and PSO algorithms have been applied to minimize the fitness function value, thus estimating optimal parameter vectors.…”