OmeGA: A Competent Genetic Algorithm for Solving Permutation and Scheduling Problems

by Dimitri Knjazew

Overview

OmeGA: A Competent Genetic Algorithm for Solving Permutation and Scheduling Problems addresses two increasingly important areas in GA implementation and practice. OmeGA, or the ordering messy genetic algorithm, combines some of the latest in competent GA technology to solve scheduling and other permutation problems. Competent GAs are those designed for principled solutions of hard problems, quickly, reliably, and accurately. Permutation and scheduling problems are difficult combinatorial optimization problems with commercial import across a variety of industries.

This book approaches both subjects systematically and clearly. The first part of the book presents the clearest description of messy GAs written to date along with an innovative adaptation of the method to ordering problems. The second part of the book investigates the algorithm on boundedly difficult test functions, showing principled scale up as problems become harder and longer. Finally, the book applies the algorithm to a test function drawn from the literature of scheduling.

Product Details

ISBN-13:	9780792374602
Publisher:	Springer US
Publication date:	01/31/2002
Series:	Genetic Algorithms and Evolutionary Computation , #6
Edition description:	2002
Pages:	152
Product dimensions:	6.10(w) x 9.25(h) x 0.24(d)

Table of Contents

1. Development of the Omega.- 1.1 The Mechanics of the Fast Messy GA.- 1.2 Using Random Keys for Representation.- 1.3 Designing the OmeGA.- 1.4 Ordering Deceptive Problems.- 1.5 Problem Codings.- 1.6 Experiments.- 1.7 Summary.- 2. Performance Analysis of the Omega.- 2.1 Scale-up Analysis.- 2.2 New Ordering Deceptive Problems.- 2.3 Tests with Uniform and Nonuniform Scaling.- 2.4 Test with Nonuniform Building-Block Size.- 2.5 Tests with Overlapping Building Blocks.- 2.6 Summary.- 3. Application to a Scheduling Problem.- 3.1 Introduction to Scheduling Problems.- 3.2 Problem Formulation.- 3.3 Schedule Representation and Decoding.- 3.4 Experiments.- 3.5 Summary.- 4. Conclusions and Future Work.- Appendices.- Appendix A: The Benchmark Input Data.- Appendix B: Best Schedules.- Appendix C: Source Code of OmeGA.- References.

Editorial Reviews

In this text, Knjazew (SAP AG, Germany) develops a permutation- oriented competent genetic algorithm (GA) and demonstrates its performance and scalability on hard permutation problems. Coverage includes background information about competent GAs; development of the ordering messy genetic algorithm (OmeGA); a detailed scalability and performance analysis of the method; application of the OmeGA to a real world scheduling problem that has been used as a standard benchmark within SAP (a leading German enterprise resource planning software vendor); and suggestions for future research in this area. Requires a basic knowledge of GAs. This book could be used in classes on genetic and evolutionary computation, and in operations research. Annotation c. Book News, Inc., Portland, OR (booknews.com)