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Full Description
Advances and Trends in Genetic Programming, Volume OneTechniques and Life Cycles 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.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.
Contents
Section 11. Introduction on Machine Learning, Genetic programming life cycles, and classification in multi class problems2. Inter-comparison of different types of machine learning algorithm for classification3. Two class versus multi-class classification for numeric data4. Types of genetic programming and their applicationsSection 2: Tree-Based Genetic Programming5. Tree-based Genetic programming for Classification6. Diversity in initial population of Genetic programming7. Intron in Genetic programming8. The problem of Bloat in Genetic Programming: Effects of bloat on the Classifier evolvementSection 3: Crossover and Mutation Operators in Genetic Programming9. Dynamic Fitness Evaluation: It's effects on training paradigm10. Crossover and Mutation Operators: How they Work in Parallel to Improve the Genetic Programming Life Cycle11. An Integrated model-based Genetic Programming Algorithm for the Multi-class Classification
