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Handbook of Genetic Programming Applications pp 99–140Cite as

Genetic Programming Applications in Chemical Sciences and Engineering

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Abstract

Genetic programming (GP) (Koza, Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems, Stanford University, Stanford, 1990) was originally proposed for automatically generating computer programs that would perform pre-defined tasks. There exist two other important GP applications, namely classification and “symbolic regression” that are being utilized widely in pattern recognition and data-driven modeling, respectively. As compared to the classification, GP has found more applications for its capability to effectively perform symbolic regression (SR). Given an input–output data set SR can search and optimize an appropriate linear/non-linear data-fitting function and all its parameters. The GP-based symbolic regression (GPSR) offers an attractive avenue to extract correlations, explore candidate models and provide optimal solutions to the data-driven modeling problems. Despite its novelty and effectiveness, GP—unlike artificial neural networks and support vector regression—has not seen an explosive growth in its applications. Owing to the availability of feature-rich and user-friendly software packages as also faster computers (including parallel computing devices), there has been a spate of research publications in recent years exploiting the significant potential of GP for diverse classification and modeling applications in chemistry and related sciences and engineering. Accordingly, this chapter provides a bird’s eye-view of the ever increasing applications of GP in the chemical sciences and engineering with the objective of bringing out its immense potential in solving diverse problems. The present chapter not only focuses on the important GP-applications but also offers guidelines to develop optimal GP models. Additionally, a non-exclusive list of GP software packages is provided.

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Authors and Affiliations

  1. Artificial Intelligence Systems Group, Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 41100, India

    Renu Vyas, Purva Goel & Sanjeev S. Tambe

Authors
  1. Renu Vyas
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  2. Purva Goel
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  3. Sanjeev S. Tambe
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Correspondence to Sanjeev S. Tambe .

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Editors and Affiliations

  1. BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, Michigan, USA

    Amir H. Gandomi

  2. Department of Civil & Environmental Engineering, Michigan State University, East Lansing, Michigan, USA

    Amir H. Alavi

  3. Department of Computer Science and Information Systems, University of Limerick, Limerick, Limerick, Ireland

    Conor Ryan

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Vyas, R., Goel, P., Tambe, S.S. (2015). Genetic Programming Applications in Chemical Sciences and Engineering. In: Gandomi, A., Alavi, A., Ryan, C. (eds) Handbook of Genetic Programming Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-20883-1_5

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  • DOI: https://doi.org/10.1007/978-3-319-20883-1_5

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