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Computer and Information Science » Artificial Intelligence
Genetic Programming - New Approaches and Successful Applications
Edited by Sebastian Ventura, ISBN 978-953-51-0809-2, Hard cover, 284 pages, Publisher: InTech, Chapters published October 18, 2012 under CC BY 3.0 license
DOI: 10.5772/3102
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.
- Chapter 1
Using Quantitative Genetics and Phenotypic Traits in Genetic Programming - Chapter 2
Continuous Schemes for Program Evolution - Chapter 3
Programming with Annotated Grammar Estimation - Chapter 4
Genetically Programmed Regression Linear Models for Non-Deterministic Estimates - Chapter 5
Parallel Genetic Programming on Graphics Processing Units - Chapter 6
Structure-Based Evolutionary Design Applied to Wire Antennas - Chapter 7
Dynamic Hedging Using Generated Genetic Programming Implied Volatility Models - Chapter 8
The Usage of Genetic Methods for Prediction of Fabric Porosity - Chapter 9
Genetic Programming: A Novel Computing Approach in Modeling Water Flows - Chapter 10
Genetic Programming: Efficient Modeling Tool in Hydrology and Groundwater Management - Chapter 11
Comparison Between Equations Obtained by Means of Multiple Linear Regression and Genetic Programming to Approach Measured Climatic Data in a River - Chapter 12
Inter-Comparison of an Evolutionary Programming Model of Suspended Sediment Time-Series with Other Local Models
