Genetic Algorithms in Optimisation, Simulation and Modelling / Edition 1

by J. Stender, E. Hillebrand, J. Kingdon

Overview

One common criticism of Artificial Intelligence (AI) is the brittleness of the solutions it produces. The suggestion is that AI systems have not scaled well beyond the relatively limited domains to which they have been applied. In recent years there has been a marked trend in the AI community towards real-world applications. Techniques, inspired by AI's wider ambition to produce more intelligent machines, are not only gaining acceptance in other fields of scientific research, but also in areas such as business, commerce and industry. Moreover, there is a tendency for the techniques themselves to be developed, tested and refined within such applications. The contemporary theme seems to be, if a technique represents a genuine advance in software engineering, then by definition it has commercial advantage. Nowhere is this trend more evident than in the application of genetic algorithms (GAs). What has marked out as GAs as compared to other techniques is the surprising speed with which commercial organisations have shown an interest. One of the reasons for this is that GAs seem to offer an extremely effective, general-purpose, means for dealing with both complexity and scale. This book present a snapshot of some of the GA research taking place within Europe. In summery, the book attempts to emphasise the diversity of the GA approach by presenting detailed descriptions of GAs used for real-world optimisation and for complex modelling problems.

Product Details

ISBN-13:	9789051991802
Publisher:	IOS Press
Publication date:	07/15/2005
Series:	Frontiers in Artificial Intelligence and Applications Series , #23
Pages:	272
Product dimensions:	7.00(w) x 10.00(h) x 0.57(d)

Table of Contents

Preface

Introduction to Genetic Algorithm Programming Environments ..... 1

Development Needs for Diverse Genetic Algorithm Design ..... 9

The Theory of Breeding and the Breeder Genetic Algorithm ..... 27

The Reproductive Plan Language RPL2: Motivation, Architecture and

Applications ..... 65

Parallel Genetic Approach to Solve Automatic Process Allocation

Within Massively Parallel Systems ..... 95

GAME's Parallel Programming Model ..... 111

Genetic Algorithms in Business ..... 157

Genetic Rule Induction for Financial Decision Making ..... 185

Searching Favorable Protein Conformations with a Genetic Algorithm

and a Multi-Value Fitness Function ..... 202

Standort 2000: Local Modelling of the Brandenburg Area Using Genetic Algorithms ..... 219

Author Index ..... 261