Current strategy of non-model-based bioprocess optimizations with genetic algorithms in bioscience - A systematic review

Abstract

This article describes genetic algorithms (GAs), a widely used group of nature-inspired metaheuristics, and presents examples of their application in model-free optimization of bioprocesses. This approach is mainly used to solve optimization problems expressed through mathematical models. However, there are many situations in which laboratory optimization with GAs can be performed. In many cases, GAs have been reported to be superior to other popular optimization methods. Hence, their use is particularly recommended when multiple variables need to be studied simultaneously, the search space is large, and/or little is known about the interactions between individual factors. Despite their usefulness and simplicity, the number of reported experimental examples of non-model-based optimization using GAs remains limited. Real-world experimental evaluations, as opposed to mathematical fitness functions, are neither classified nor explicitly defined in the literature. The authors propose the term “Reality-Based Genetic Algorithms” and express hope for its widespread adoption. There is a significant need for both theoretical and empirical research on the parameter configurations of genetic algorithms for experimental optimization, and the authors anticipate that this gap will be addressed in the future. In the meantime, it is recommended to either use configurations that have been proven successful in similar studies or to experiment with different configurations to generate comparative data for future research.