Издательство InTech, 2012, -298 pp.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. It consists of twelve openly solicited chapters, written by international researchers and leading experts in the field of GP.
The book is organized in two sections. The first section (chapters 1 to 5) introduces a new theoretical framework (the use of quantitative genetics and phenotypic traits – chapter 1) to analyse the behaviour of GP algorithms. Furthermore, the section contains three new GP proposals: the first one is based on the use of continuous values for the representation of programs (chapter 2), the second is based on the use of estimation of distribution algorithms (chapter 3), and the third hybridizes the use of GP with statistical models in order to obtain and formally validate linear regression models (chapter 4). The section ends with a nice introduction about the implementation of GP algorithms on graphics processing units (chapter 5).
The second section of the book (chapters 6 to 12) shows several successful examples of the application of GP to several complex real-world problems. First of these applications is the use of GP in the automatic design of wireless antennas (chapter 6). The two following chapters show two interesting examples of industrial applications: the forecasting of the volatility of materials (chapter 7) and the prediction of fabric porosity (chapter 8). In both chapters GP models outperformed the results yield by the state-of-the art methods. The next three chapters are related to the application of GP to modelling water flows, being the first of them a gentle introduction to the topic (chapter 9) and the following two remarkable case studies (chapters 10 and 11). The last chapter of the book (chapter 12) shows the application of GP to an interesting time series modelling problem: the estimation of suspended sediment loads in the Mississippi river.
The volume is primarily aimed at postgraduates, researchers and academics. Nevertheless, it is hoped that it may be useful to undergraduates who wish to learn about the leading techniques in GP.Section 1 New Approaches
Using Quantitative Genetics and Phenotypic Traits in Genetic Programming
Continuous Schemes for Program Evolution
Programming with Annotated Grammar Estimation
Genetically Programmed Regression Linear Models for Non-Deterministic Estimates
Parallel Genetic Programming on Graphics Processing Units
Section 2 Successful Applications
Structure-Based Evolutionary Design Applied to Wire Antennas
Dynamic Hedging Using Generated Genetic Programming Implied Volatility Models
The Usage of Genetic Methods for Prediction of Fabric Porosity
Genetic Programming: A Novel Computing Approach in Modeling Water Flows
Genetic Programming: Efficient Modeling Tool in Hydrology and Groundwater Management
Comparison Between Equations Obtained by Means of Multiple Linear Regression and Genetic Programming to Approach Measured Climatic Data in a River
Inter-Comparison of an Evolutionary Programming Model of Suspended Sediment Time-Series with Other Local Models
