Genetic Programming Theory and Practice
Genetic Programming Theory and Practice explores the emerging interaction between theory and practice in the cutting-edge, machine learning method of Genetic Programming (GP). The material contained in this contributed volume was developed from a workshop at the University of Michigan's Center...
| Other Authors: | , |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
New York, NY
Springer US
2003, 2003
|
| Edition: | 1st ed. 2003 |
| Series: | Genetic Programming
|
| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- 1 Genetic Programming: Theory and Practice
- 2 An Essay Concerning Human Understanding for Genetic Programming
- 3 Classification of Gene Expression Data with Genetic Programming
- 4 Artificial Regulatory Networks and Genetic Programming
- 5 Using Software Engineering Knowledge to Drive Genetic Program Design Using Cultural Algorithms
- 6 Continuous Hierarchical Fair Competition Model for Sustainable Innovation in Genetic Programming
- 7 What Makes a Problem GP-Hard?
- 8 A Probalistic Model of Size Drift
- 9 Building-Block Supply in Genetic Programming
- 10 Modularization by Multi-Run Frequency Driven Subtree Encapsulation
- 11 The Distribution of Reversible Functions is Normal
- 12 Doing Genetic Algorithms the Genetic Programming Way
- 13 Probalistic Model Building and Competent Genetic Programming
- 14 Automated Synthesis by Means of Genetic Programming Complex Structures Incorporating Reuse, Parameterized Reuse, Hierarchies, and Development
- 15 Industrial Strength Genetic Programming
- 16 Operator Choice and the Evolution of Robust Solution
- 17 A Hybrid GP-Fuzzy Approach for Reservoir Characterization
- 18 Enhanced Emerging Market Stock Selection
- 19 Three Fundamentals of the Biological Genetic Algorithm