Development and validation of evolutionary algorithm software as an optimization tool for biological and environmental applications.
A flexible, extendable tool for the optimization of (micro)biological processes and protocols using evolutionary algorithms was developed. It has been tested using three different theoretical optimization problems: 2 two-dimensional problems, one with three maxima and one with five maxima and a river autopurification optimization problem with boundary conditions. For each problem, different evolutionary parameter settings were used for the optimization. For each combination of evolutionary parameters, 15 generations were run 20 times. It has been shown that in all cases, the evolutionary algorithm gave rise to valuable results. Generally, the algorithms were able to detect the more stable sub-maximum even if there existed less stable maxima. The latter is, from a practical point of view, generally more desired. The most important factors influencing the convergence process were the parameter value randomization rate and distribution. The developed software, described in this work, is available for free.
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Laboratory of Microbial Ecology and Technology, Ghent University, Ghent 9000, Belgium.
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D000465:Algorithms; D004463:Ecology / Q000379:methods*; D008954:Models, Biological; D012984:Software