Author Description

<p><i>Advances and Trends in Genetic Programming, Volume One: Classification Techniques and Life Cycles</i> presents the reader with complete coverage of the most current developments in Genetic Programming for Artificial Intelligence. The book provides a thorough look at classification as a systematic way of predicting class membership for a set of examples or instances using the properties of those examples. Classification arises in a wide variety of real life situations, such as detecting faces from large database, finding vehicles, matching fingerprints and diagnosing medical conditions.</p> <p>A classification algorithm requires huge amount of accuracy and reliability that is very difficult for human programmers. Therefore, there is a need to develop an automated computer-based classification system that can classify the required objects.</p><ul> <li>Presents the latest advances in Genetic Programming for Artificial Intelligence</li> <li>Discusses automated computer-based classification algorithms and systems, including comparison of different types of machine learning and two-class versus multi-class classification</li> <li>Includes discussions of tree-based Genetic Programming, the Intron problem, Dynamic Fitness Evaluation, Crossover and Mutation Operators, and a presentation of an integrated model-based Genetic Programming Algorithm for multi-class classification</li></ul>