Scatter Search: Methodology and Implementation in C

The evolutionary approach called scatter search originated from strategies for creating composite decision rules and surrogate constraints. Recent studies demonstrate the practical advantages of this approach for solving a diverse array of optimization problems from both classical and real world settings. Scatter search contrasts with other evolutionary procedures, such as genetic algorithms, by providing unifying principles for joining solutions based on generalized path constructions in Euclidean space and by utilizing strategic designs where other approaches resort to randomization. The book's goal is to provide the basic principles and fundamental ideas that will allow the readers to create successful applications of scatter search. The book includes the C source code of the methods introduced in each chapter.

From the Foreword:
'Scatter Search represents a "missing link" in the literature of evolutionary methods... From a historical perspective, the dedicated use of heuristic strategies both to guide the process of combining solutions and to enhance the quality of offspring has been heralded as a key innovation in evolutionary methods, giving rise to what are sometimes called "hybrid" or ("memetic") evolutionary procedures. The underlying processes have been introduced into the mainstream of evolutionary methods (such as genetic algorithms, for example) by a series of gradual steps beginning in the late 1980s. Yet this theme is an integral part of the scatter search methodology proposed a decade earlier, and the form and scope of such heuristic strategies embedded in scatter search continue to set it apart. Although there are points in common between scatter search and other evolutionary approaches, principally as a result of changes that have brought other approaches closer to scatter search in recent years, there remain differences that have an important impact on practical outcomes. Reflecting this impact, a hallmark of the present book is its focus on practical problem solving. Laguna and Martí give the reader the tools to create scatter search implementations for problems from a wide range of settings. Although theoretical problems (such as abstract problems in graph theory) are included, beyond a doubt the practical realm has a predominant role in this book....'
Fred Glover, University of Colorado

Intended as a reference for researchers and practitioners interested in gaining detailed understanding of scatter search with the goal of expanding and applying this methodology, as well as being suitable for a graduate-level seminar on metaheuristic optimization, this volume begins with three tutorial chapters. Advanced scatter search design is next addressed, followed by a summary of tabu search concepts that are linked with scatter search. Connections with other population-based approaches are then addressed, as well as scatter search applications; commercial scatter search implementation; and experiences, findings, and future directions. Annotation ©2003 Book News, Inc., Portland, OR