• Title Genetic Programming - New Approaches and Successful Applications
• Author(s) Sebastian Ventura
• Publisher: IN-TECH (October 18, 2012)
• License(s): Attribution 3.0 Unported (CC BY 3.0)
• Hardcover 284 pages
• eBook PDF Files, and a zipped PDF, 6.48 MB
• Language: English
• ISBN-10: N/A
• ISBN-13: 978-953-51-0809-2
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Book Description

Genetic programming (GP) is a branch of Evolutionary Computing that aims the automatic discovery of programs to solve a given problem. Since its appearance, in the earliest nineties, GP has become one of the most promising paradigms for solving problems in the artificial intelligence field, producing a number of human-competitive results and even patentable new inventions. And, as other areas in Computer Science, GP continues evolving quickly, with new ideas, techniques and applications being constantly proposed.

The purpose of this book is to show recent advances in the field of GP, both the development of new theoretical approaches and the emergence of applications that have successfully solved different real world problems. The volume is primarily aimed at postgraduates, researchers and academics, although it is hoped that it may be useful to undergraduates who wish to learn about the leading techniques in GP.

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• Amazon

• Artificial Intelligence, Machine Learning, and Logic Programming
• Operations Research (OR), Linear Programming, Optimization, and Approximation
• Algorithms and Data Structures
• Computational and Algorithmic Mathematics
• Computational Complexity
• Discrete Mathematics

• Genetic Programming - New Approaches and Successful Applications (Sebastian Ventura)
• PDF Format

• A Field Guide to Genetic Programming (Riccardo Poli, et al)

  This book provides a complete and coherent review of the theory of Genetic Programming (GP), written by three of the most active scientists in GP. GP solves problems without the user having to know or specify the form or structure of solutions in advance.

• Genetic Algorithms and Genetic Programming: Concepts and Apps

  This book discusses algorithmic developments in the context of Genetic Algorithms (GAs) and Genetic Programming (GP). It applies the algorithms to significant combinatorial optimization problems and algorithm development.

• Real-World Applications of Genetic Algorithms (Olympia Roeva)

  This book presents hybrid techniques based on Artificial Neural Network, Fuzzy Sets, Automata Theory, other metaheuristic or classical algorithms, etc. It examines various examples of algorithms in different real-world application domains.

• Genetic and Evolutionary Computation for Image Processing

  Image analysis and processing is steadily gaining relevance within the large number of application fields to which genetic and evolutionary computation (GEC) techniques are applied. This book is the first attempt to offer a panoramic view on the field.

• Genetic Algorithms in Applications (Rustem Popa)

  This well-organized book takes the reader through the new and rapidly expanding field of genetic algorithms step by step, from a discussion of numerical optimization, to a survey of current extensions to genetic algorithms and applications.

• Essentials of Metaheuristics (Sean Luke)

  This book is an open set of lecture notes on metaheuristics algorithms. The algorithmic family includes genetic algorithms, hill-climbing, simulated annealing, ant colony optimization, particle swarm optimization, and so on.

• Bio-Inspired Computational Algorithms and Their Applications

  Bio-inspired computational algorithms are always topics in artificial intelligence. Integrates contrasting techniques of genetic algorithms, artificial immune systems, particle swarm optimization, and hybrid models to solve many real-world problems.

• Evolutionary Algorithms (Eisuke Kita)

  The goal of this book is to provide effective evolutionary algorithms that have been used as an experimental framework within biological evolution and natural selection in the field of artificial life.

• Advances in Evolutionary Algorithms (Witold Kosinski)

  Provide effective optimization algorithms for solving a broad class of problems quickly, accurately, and reliably by employing evolutionary mechanisms.

• Global Optimization Algorithms - Theory and Application

  This book is devoted to global optimization algorithms, which are methods to find optimal solutions for given problems. It especially focuses on Evolutionary Computation by discussing evolutionary algorithms, genetic algorithms, Genetic Programming, etc.