"Genetic Programming: New Approaches and Successful Applications" ed. by Sebastián Ventura

The purpose of this book is to show recent advances in the field of Genetic programming (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.

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.

Contents
Preface
Section 1 New Approaches
1 Using Quantitative Genetics and Phenotypic Traits in Genetic Programming
2 Continuous Schemes for Program Evolution
3 Programming with Annotated Grammar Estimation
4 Genetically Programmed Regression Linear Models for Non-Deterministic Estimates
5 Parallel Genetic Programming on Graphics Processing Units
Section 2 Successful Applications
6 Structure-Based Evolutionary Design Applied to Wire Antennas
7 Dynamic Hedging Using Generated Genetic Programming Implied Volatility Models
8 The Usage of Genetic Methods for Prediction of Fabric Porosity
9 Genetic Programming: A Novel Computing Approach in Modeling Water Flows
10 Genetic Programming: Efficient Modeling Tool in Hydrology and Groundwater Management
11 Comparison Between Equations Obtained by Means of Multiple Linear Regression and Genetic Programming to Approach Measured Climatic Data in a River
12 Inter-Comparison of an Evolutionary Programming Model of Suspended Sediment Time-Series with Other Local Models