Genetic Programming Assisted Stochastic Optimization Strategies for Optimization of Glucose to Gluconic Acid Fermentation

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@Article{cheema:2002:BTP,
  author =       "Jitender Jit Singh Cheema and Narendra V. Sankpal and 
                 Sanjeev S. Tambe and Bhaskar D. Kulkarni",
  title =        "Genetic Programming Assisted Stochastic Optimization
                 Strategies for Optimization of Glucose to Gluconic Acid
                 Fermentation",
  journal =      "Biotechnology Progress",
  year =         "2002",
  volume =       "18",
  number =       "6",
  pages =        "1356--1365",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "8756-7938",
  URL =          "http://www3.interscience.wiley.com/journal/121399381/abstract",
  DOI =          "doi:10.1021/bp015509s",
  abstract =     "This article presents two hybrid strategies for the
                 modeling and optimization of the glucose to gluconic
                 acid batch bioprocess. In the hybrid approaches, first
                 a novel artificial intelligence formalism, namely,
                 genetic programming (GP), is used to develop a process
                 model solely from the historic process input-output
                 data. In the next step, the input space of the GP-based
                 model, representing process operating conditions, is
                 optimized using two stochastic optimization (SO)
                 formalisms, viz., genetic algorithms (GAs) and
                 simultaneous perturbation stochastic approximation
                 (SPSA). These SO formalisms possess certain unique
                 advantages over the commonly used gradient-based
                 optimization techniques. The principal advantage of the
                 GP-GA and GP-SPSA hybrid techniques is that process
                 modeling and optimization can be performed exclusively
                 from the process input-output data without invoking the
                 detailed knowledge of the process phenomenology. The
                 GP-GA and GP-SPSA techniques have been employed for
                 modeling and optimization of the glucose to gluconic
                 acid bioprocess, and the optimized process operating
                 conditions obtained thereby have been compared with
                 those obtained using two other hybrid
                 modeling-optimization paradigms integrating artificial
                 neural networks (ANNs) and GA/SPSA formalisms. Finally,
                 the overall optimized operating conditions given by the
                 GP-GA method, when verified experimentally resulted in
                 a significant improvement in the gluconic acid yield.
                 The hybrid strategies presented here are generic in
                 nature and can be employed for modeling and
                 optimization of a wide variety of batch and continuous
                 bioprocesses.",
  notes =        "

                 PMID: 12467472 [PubMed - indexed for MEDLINE]
                 S8756-7938(01)05509-6

                 ACS Publications Division, American Chemical Society
                 and American Institute of Chemical Engineers

                 Chemical Engineering Division, National Chemical
                 Laboratory, Pune 411008, India",
}

Genetic Programming entries for Jitender Jit Singh Cheema Narendra V Sankpal Sanjeev S Tambe Bhaskar D Kulkarni

Citations