title: Application of Hybrid Genetic Algorithm Routine in Optimizing Food and Bioengineering Processes.
abstract: Optimization is a crucial step in the analysis of experimental results. Deterministic methods only converge on local optimums and require exponentially more time as dimensionality increases. Stochastic algorithms are capable of efficiently searching the domain space; however convergence is not guaranteed. This article demonstrates the novelty of the hybrid genetic algorithm (HGA), which combines both stochastic and deterministic routines for improved optimization results. The new hybrid genetic algorithm developed is applied to the Ackley benchmark function as well as case studies in food, biofuel, and biotechnology processes. For each case study, the hybrid genetic algorithm found a better optimum candidate than reported by the sources. In the case of food processing, the hybrid genetic algorithm improved the anthocyanin yield by 6.44%. Optimization of bio-oil production using HGA resulted in a 5.06% higher yield. In the enzyme production process, HGA predicted a 0.39% higher xylanase yield. Hybridization of the genetic algorithm with a deterministic algorithm resulted in an improved optimum compared to statistical methods.
authors.affiliation: Biofuels Department, Energy and Environment Directorate, Idaho National Laboratory, 750 University Boulevard, MS 3570, Idaho Falls, ID 83415, USA. jayashankar.tumuluru@inl.gov.
authors.forename: Jaya Shankar
authors.initials: JS
authors.lastname: Tumuluru
authors.affiliation: Biofuels Department, Energy and Environment Directorate, Idaho National Laboratory, 750 University Boulevard, MS 3570, Idaho Falls, ID 83415, USA.
authors.forename: Richard
authors.initials: R
authors.lastname: McCulloch
keywords: Ackley function; anthocyanin yield; fatty acid methyl ester; hybrid genetic algorithm; optimization; response surface functions; xylanase activity