Description

Book Synopsis
Data is more valuable than ever in the twenty-first century, and tremendous amounts of data are being generated every second. With a fast-growing information industry, engineers are required to develop new tools and techniques that increase human capabilities of mining useful knowledge from the vast amounts of data.

Optimized Genetic Programming Applications: Emerging Research and Opportunities is an essential reference source that explores the concept of genetic programming and its role in managing engineering problems. It also examines genetic programming as a supervised machine learning technique, focusing on implementation and application. As a resource that details both the theoretical aspects and implementation of genetic programming, this book is a useful source for academicians, biological engineers, computer programmers, scientists, researchers, and upper-level students seeking the latest research on genetic programming.

Optimized Genetic Programming Applications: Emerging Research and Opportunities

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    £200.70

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    RRP £223.00 – you save £22.30 (10%)

    Order before 4pm tomorrow for delivery by Wed 17 Jun 2026.

    A Hardback by Bahrudin Hrnjica, Ali Danandeh Mehr

    15 in stock


      View other formats and editions of Optimized Genetic Programming Applications: Emerging Research and Opportunities by Bahrudin Hrnjica

      Publisher: IGI Global
      Publication Date: 30/07/2018
      ISBN13: 9781522560050, 978-1522560050
      ISBN10: 152256005X

      Description

      Book Synopsis
      Data is more valuable than ever in the twenty-first century, and tremendous amounts of data are being generated every second. With a fast-growing information industry, engineers are required to develop new tools and techniques that increase human capabilities of mining useful knowledge from the vast amounts of data.

      Optimized Genetic Programming Applications: Emerging Research and Opportunities is an essential reference source that explores the concept of genetic programming and its role in managing engineering problems. It also examines genetic programming as a supervised machine learning technique, focusing on implementation and application. As a resource that details both the theoretical aspects and implementation of genetic programming, this book is a useful source for academicians, biological engineers, computer programmers, scientists, researchers, and upper-level students seeking the latest research on genetic programming.

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