Main description:

Advances and Trends in Genetic Programming, Volume One: Classification Techniques 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 1: Overview on Machine Learning
1. Introduction on Machine Learning, Genetic programming life cycles, and classification in multi class problems
2. Inter-comparison of different types of machine learning algorithm for classification
3. Two class versus multi-class classification for numeric data
4. Types of genetic programming and their applications

Section 2: Tree-Based Genetic Programming
5. Tree-based Genetic programming for Classification
6. Diversity in initial population of Genetic programming
7. Intron in Genetic programming
8. The problem of Bloat in Genetic Programming: Effects of bloat on the Classifier evolvement

Section 3: Crossover and Mutation Operators in Genetic Programming
9. Dynamic Fitness Evaluation: It's effects on training paradigm
10. Crossover and Mutation Operators: How they Work in Parallel to Improve the Genetic Programming Life Cycle
11. An Integrated model-based Genetic Programming Algorithm for the Multi-class Classification