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Handbook of genetic programming applications
Gandomi A., Alavi A., Ryan C., Springer International Publishing, New York, NY, 2015. 593 pp. Type: Book (978-3-319208-82-4)
Date Reviewed: Feb 23 2017

Over the years, researchers have attempted to solve tough scientific problems using techniques from the field of artificial intelligence. One such collection of techniques is genetic programming (GP), which is a branch of evolutionary computation in which program code or mathematical formulae are evolved to solve some problem of interest. Evolutionary computing is the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. Early GP systems were implemented in the LISP programming language and evolved LISP programs as solutions. In these systems, programs were denoted as tree expressions, and the evolutionary process necessitated the generation and manipulation of these trees. Present-day GP systems use an assortment of internal representations and target languages. This handbook on GP concentrates on the applications of GP to many real-world problems.

The handbook has 23 chapters contributed by many practitioners of GP. The book is divided into four parts: an overview of GP applications, specialized applications, hybrid approaches, and tools.

The first part provides a short overview of GP applications. The seven chapters in this part cover several interesting applications of GP. This includes graph-based evolutionary art, modeling of geotechnical systems, and applications of GP to hydrology; hydraulic engineering; chemical sciences; and electrical engineering. The last chapter of this part deals with the choice of mates in evolutionary computation.

The second part, on specialized applications, forms the bulk of the book. There are 11 chapters in this part. The topics handled are as varied as genetically improved software, the design of real-time computer-based systems, image classification and breast cancer detection, ground motion prediction with applications to earthquake engineering, soil liquefaction hazard evaluation, geotechnical site characterization, simulation of geochemical transport in contaminated mine sites, prediction of droughts, modeling of concrete, mining association rules in relational databases, and the classification of streaming data.

The third part is on hybrid approaches. The three chapters of this part focus on applications of GP to geotechnical and geo-environmental modeling, medicinal chemistry and toxicology, and the securities market.

The fourth part of the book deals with tools. The two chapters of this part discuss GPTIPS 2, open-source software for symbolic data mining, and eCrash, a testing tool for object-oriented software.

The editors of this handbook have done a good job in producing a consistent book despite the diversity of topics and contributions. The chapters of the book are self-contained and may be read independently of other chapters. The online versions of many of the chapters are said to contain supplementary material. The chapters of the handbook include many references to the literature. However, there is no index for the book. The publisher has made available hardback and e-book versions of the book. Individual chapters of the handbook are also available for purchase. The handbook is timely but addresses just a few of the many emerging applications of GP to real-life problems. GP is an evolving field with enormous potential. It is an active area of research. I strongly recommend this handbook for engineers, practitioners, computer science researchers, and libraries. It will be suitable for advanced students for selecting research problems. It will surely help experienced practitioners seeking to apply GP to new areas.

Reviewer:  S. V. Nagaraj Review #: CR145077 (1705-0246)
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