Tuning Genetic Algorithm Parameters to Improve Convergence Time

Abstract

Fermentation processes by nature are complex, time-varying, and highly nonlinear. As dynamic systems their modeling and further high-quality control are a serious challenge. The conventional optimization methods cannot overcome the fermentation processes peculiarities and do not lead to a satisfying solution. As an alternative, genetic algorithms as a stochastic global optimization method can be applied. For the purpose of parameter identification of a fed-batch cultivation ofS. cerevisiaealtogether four kinds of simple and four kinds of multipopulation genetic algorithms have been considered. Each of them is characterized with a different sequence of implementation of main genetic operators, namely, selection, crossover, and mutation. The influence of the most important genetic algorithm parameters—generation gap, crossover, and mutation rates has—been investigated too. Among the considered genetic algorithm parameters, generation gap influences most significantly the algorithm convergence time, saving up to 40% of time without affecting the model accuracy.

Parameters identification of a photovoltaic module in a thermal system using meta-heuristic optimization methods

“…ð10Þ Tables 3 and 4 summarize the tuning parameters of the GA and PSO algorithm, which are given according to some guidelines proposed in [23][24][25]:…”

Kinetic parameters estimation of protease production using penalty function method with hybrid genetic algorithm and particle swarm optimization

“…[22] Production of protease was carried out in 500-mL shake-flasks with 100 mL medium containing the follow- 4 and 0.07 g/L CaCl 2 . Separately sterilized glucose was added into the medium as a carbon source just before inoculation.…”

“…Genetic algorithms (GAs), originally developed by Holland in 1975, [3] are quite promising as a stochastic global optimization method. [4] GA belongs to the larger class of evolutionary algorithms, which use an approach inspired by natural evolution, e.g. mutation, selection and crossover, to solve optimization problems.…”

Comparison across growth kinetic models of alkaline protease production in batch and fed-batch fermentation using hybrid genetic algorithm and particle swarm optimization

“…[10] GAs are quite promising as a stochastic global optimization method. [11] They are adaptive heuristic search algorithms based on the evolutionary ideas of natural selection and genetics. [12] GA is perhaps the most popular method used in the parameter estimation problem.…”

“…Properties such as noise tolerance and ease of interfacing and hybridization make GA a suitable method for the identification of parameters in fermentation models. [11] Particle swarm optimization (PSO) is also an evolutionary algorithm first proposed by Kennedy and Eberhart in 1995. [14] The theoretical fundamental of PSO is the social influence and social learning mechanism of a kind of social psychological modal motivated by the behaviour of organisms such as fish schooling and bird flocking.…”