This book constitutes the refereed proceedings of the 6th European Conference on Genetic Programming, EuroGP 2003, held in Essex, UK in April 2003. The 45 revised papers presented were carefully reviewed and selected from 61 submissions. All current aspects of genetic programming and genetic algorithms are addressed, ranging from foundational, theoretical, and methodological issues to advanced applications in various fields.

This seminal text of Computer Science, the most cited book on the subject, is now available for the first time in paperback. Constraint satisfaction is a decision problem that involves finite choices. It is ubiquitous. The goal is to find values for a set of variables that will satisfy a given set of constraints. It is the core of many applications in artificial intelligence, and has found its application in many areas, such as planning and scheduling. Because of its generality, most AI researchers should be able to benefit from having good knowledge of techniques in this field. Originally published in 1993, this now classic book was the first attempt to define the scope of constraint satisfaction. It covers both the theoretical and the implementation aspects of the subject. It provides a framework for studying this field, relates different research, and resolves ambiguity in a number of concepts and algorithms in the literature. This seminal text is arguably the most rigorous book in the field. All major concepts were defined in First Order Predicate Calculus. Concepts defined this way are precise and unambiguous.

Simulation in Computational Finance and Economics: Tools and Emerging Applications presents a thorough collection of works, covering several rich and highly productive areas of research including Risk Management, Agent-Based Simulation, and Payment Methods and Systems, topics that have found new motivations after the strong recession experienced in the last few years. Despite the fact that simulation is widely accepted as a prominent tool, dealing with a simulation-based project requires specific management abilities of the researchers. Economic researchers will find an excellent reference to introduce them to the computational simulation models. The works presented in this book can be used as an inspiration for economic researchers interested in creating their own computational models in their respective fields.

This book presents three applications, based on Machine Learning and Genetic Programming, which are devoted to find useful patterns to predict future events. The objective is to train the algorithms by using past data to produce a classifier that identifies the positive cases and discriminates the false alarms. This work uses examples for predicting future opportunities in financial stock markets in cases where the number of profitable opportunities is scarce. However, when the number of positive examples is small in comparison with the number of total cases, the identification of useful patterns becomes a serious challenge. Nevertheless, the objective of many real world problems, is precisely to identify the minority class as the fraud detection problem, or medical diagnosis and many other examples. The techniques of this book are suitable to deal with imbalanced data sets, provide comprehensible results that allow users to understand the factors that are involved in the decision, as well as to generate a range of solutions that let the user choose the best trade off according to their risk preferences.