Meta-Heuristics: An Overview; I.H. Osman, J.P. Kelly. Genetic Algorithms: A Parallel Genetic Algorithm for the Set Partitioning Problem; D. Levine. Evolutionary Computation and Heuristics; Z. Michalewicz. Gene Pool Recombination in Genetic Algorithms; H. Mühlenbein, H.-M. Voigt. Genetic and Local Search Algorithms as Robust and Simple Optimization Tools; M. Yagiura, T. Ibaraki. Networks and Graphs: Comparison of Heuristic Algorithms for the Degree Constrained Minimum Spanning Tree; G. Craig, et al. An Aggressive Search Procedure for the Bipartite Drawing Problem; R. Martí. Guided Search for the Shortest Path on Transportation Networks; Y.M. Sharaiha, R. Thaiss. Scheduling and Control: A Metaheuristic for the Timetabling Problem; H. Abada, E. El-Darzi. Complex Sequencing Problems and Local Search Heuristics; P. Brucker, H. Hurink. Heuristic Algorithms for Single Processor Scheduling with Earliness and Flow Time Penalties; M. Dell'Amico, et al. Heuristics for the Optimal Control of Thermal Energy Storage; G.P. Henze, et al. Exploiting Block Structure to Improve Resource-Constrained Project Schedules; H.E. Mausser, S.R. Lawrence. Combining the Large-Step Optimization with Tabu-Search: Application to the Job-Shop Scheduling Problem; H. Ramalhinho Lourenço, M. Zwijnenburg. Job-Shop Scheduling by Simulated Annealing Combined with Deterministic Local Search; T. Yamada, R. Nakano. Simulated Annealing: Cybernetic Optimization by Simulated Annealing: An Implementation of Parallel Processing Using Probabilistic Feedback Control; M.A. Fleischer, S.H. Jacobson. A Simulated Annealing Algorithm for the Computation of Marginal Costs of TelecommunicationLinks; J.-L. Lutton, E. Philippart. Learning to Recognize (Un)Promising Simulated Annealing Runs: Efficient Search Procedures for Job Shop Scheduling and Vehicle Routing; N.M. Sadeh, S.R. Thangiah. A Preliminary Investigation into the Performance of Heuristic Search Methods Applied to Compound Combinatorial Problems; M.B. Wright, R.C. Marett. Tabu Search: Tabu Search, Combination and Integration; A.S. Al-Mahmeed. Vector Quantization with the Reactive Tabu Search; R. Battiti, et al. Tabu Thresholding for the Frequency Assignment Problem; D. Castelino, N. Stephens. A New Tabu Search Approach to the 0-1 Equicut Problem; M. Dell'Amico, F. Maffioli. Simple Tabu Thresholding and the Pallet Loading Problem; K.A. Dowsland. Critical Event Tabu Search for Multidimensional Knapsack Problems; F. Glover, G.A. Kochenberger. Solving Dynamic Stochastic Control Problems in Finance Using Tabu Search with Variable Scaling; F. Glover, et al. Comparison of Heuristics for the 0-1 Multidimensional Knapsack Problem; S. Hanafi, et al. Probabilistic Move Selection in Tabu Search for Zero-One Mixed Integer Programming Problems; A. Løkketangen, F. Glover. A Star- Shaped Diversification Approach in Tabu Search; L. Sondergeld, S. Voß. Communication Issues in Designing Cooperative Multi-Thread Parallel Searches; M. Toulouse, et al. A Study on Algorithms for Selecting Best Elements from an Array; F.T. Tseng. A Modified Tabu Thresholding Approach for the Generalised Restricted Vertex Colouring Problem; V. Valls, et al. Chunking Applied to Reactive Tabu Search; D.L. Woodruff. Tabu Search on the Geometric Traveling Salesman Problem; M. Zachariasen, M. Dam. Traveling Salesman Problems: Mixing Different Components of Metaheuristics;
Meta-heuristics have developed dramatically since their inception in the early 1980s. They have had widespread success in attacking a variety of practical and difficult combinatorial optimization problems. These families of approaches include, but are not limited to greedy random adaptive search procedures, genetic algorithms, problem-space search, neural networks, simulated annealing, tabu search, threshold algorithms, and their hybrids. They incorporate concepts based on biological evolution, intelligent problem solving, mathematical and physical sciences, nervous systems, and statistical mechanics. Since the 1980s, a great deal of effort has been invested in the field of combinatorial optimization theory in which heuristic algorithms have become an important area of research and applications.
This volume is drawn from the first conference on Meta-Heuristics and contains 41 papers on the state-of-the-art in heuristic theory and applications. The book treats the following meta-heuristics and applications: Genetic Algorithms, Simulated Annealing, Tabu Search, Networks & Graphs, Scheduling and Control, TSP, and Vehicle Routing Problems. It represents research from the fields of Operations Research, Management Science, Artificial Intelligence and Computer Science.
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