Pergamon

FOREWORD

Cmputm Ops Res. Vol. ?I. No. 8. p. 799, 1994 Elsevier Science Ltd. Prmted in Great Britain

Heuristic, Genetic and Tabu Search

Recent advances in heuristic search techniques have provided strong linkages between Operations Research and Artificial Intelligence. Heuristic search techniques that are based on “natural” systems have received much attention due to their considerable success in solving certain complex and difficult problems that have puzzled researchers for long, and due to the relative ease and generality with which they can be implemented. The most prominent of these are genetic algorithms, neural networks, simulated annealing and tabu search. These techniques borrow concepts from certain natural phenomena or process. In the case of tabu search, this natural system is the human memory process. Genetic algorithms are based on a Darwinist theory (survival of the fittest), where the strongest (or any selected) offspring in a generation is allowed to survive and reproduce. Simulated annealing is modeled after the physical process of annealing, i.e., reducing a metal’s energy level to the lowest possible state. Neural networks are inspired by the workings of the human brain. Heuristic search techniques constitute a major step forward as general problem solving approaches. They do not require strong assumptions and are applicable in a wide variety of problem settings with relatively minor modifications.

This special issue on search techniques includes 12 articles. The first article is on neural networks; the second looks at the relationships between Simulated Annealing and Tabu Search; the next four articles are applications of the tabu search technique to a variety of combinational optimization problems; the seventh article studies the role of diversification strategies in solving the Quadratic Assignment Problem; and in the last five papers genetic algorithms are used to tackle some popular, but difficult problems from the OR/MS literature. We are indebted to those who have helped us in the production of this issue. First and foremost, we thank the authors who submitted articles. Next, we thank the referees for their diligence in evaluating the articles submitted to this special issue. Their names are listed below.

ZAIYONG TANG FATEMEH (MARIAM) ZAHEDI

ERIK ROLLAND APURVA MATHUR DAVID J. POWELL GARY J. KOEHLER

GUNAR LIEPINS

M. A. VENKATARAMANAN PRABUDDHA DE JAY B. GHOSH

YUEHWERN YIH

HIROFUMI MATSUO

YIH-LONG CHANG JADRANKA SKORIN-KAPOV

J. WESLEY BARNES

Asoo J. VAKHARIA DAVE WOODRUFF

RICHARD L. DANIELS OYA ICMELI SELWYN PIRAMUTHU

SENCER YERALAN MICHAEL D. VOSE

JOHN KNOX

We also want to thank Dr Samuel J. Raff for his encouragement and patience. Finally, we would like to thank the Decision and Information Sciences Department and College of Business Administration at the University of Florida for conducting and sponsoring the “Heuristic Search” workshop where these papers were presented.

S. SELCUK ERENGUC HASAN PIRKUL Guest Editors

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