Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
Skip header Section
Search Methodologies: Introductory Tutorials in Optimization and Decision Support TechniquesOctober 2013
Publisher:
  • Springer Publishing Company, Incorporated
ISBN:978-1-4614-6939-1
Published:30 October 2013
Pages:
725
Reflects downloads up to 05 Mar 2025Bibliometrics
Skip Abstract Section
Abstract

The first edition of Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques was originally put together to offer a basic introduction to the various search and optimization techniques that students might need to use during their research, and this new edition continues this tradition. Search Methodologies has been expanded and brought completely up to date, including new chapters covering scatter search, GRASP, and very large neighborhood search. The chapter authors are drawn from across Computer Science and Operations Research and include some of the worlds leading authorities in their field. The book provides useful guidelines for implementing the methods and frameworks described and offers valuable tutorials to students and researchers in the field.As I embarked on the pleasant journey of reading through the chapters of this book, I became convinced that this is one of the best sources of introductory material on the search methodologies topic to be found. The books subtitle, Introductory Tutorials in Optimization and Decision Support Techniques, aptly describes its aim, and the editors and contributors to this volume have achieved this aim with remarkable success. The chapters in this book are exemplary in giving useful guidelines for implementing the methods and frameworks described.Fred Glover, Leeds School of Business, University of Colorado Boulder, USA[The book] aims to present a series of well written tutorials by the leading experts in their fields. Moreover, it does this by covering practically the whole possible range of topics in the discipline. It enables students and practitioners to study and appreciate the beauty and the power of some of the computational search techniques that are able to effectively navigate through search spaces that are sometimes inconceivably large. I am convinced that this second edition will build on the success of the first edition and that it will prove to be just as popular.Jacek Blazewicz, Institute of Computing Science, Poznan University of Technology and Institute of Bioorganic Chemistry, Polish Academy of Sciences

Cited By

  1. ACM
    Barzegaran M, Cervin A and Pop P Towards quality-of-control-aware scheduling of industrial applications on fog computing platforms Proceedings of the Workshop on Fog Computing and the IoT, (1-5)
  2. Kefalidou G An empirical framework for understanding human-technology interaction optimisation for route planning Proceedings of the 32nd International BCS Human Computer Interaction Conference, (1-9)
  3. ACM
    Zhang Y, Harman M, Ochoa G, Ruhe G and Brinkkemper S (2018). An Empirical Study of Meta- and Hyper-Heuristic Search for Multi-Objective Release Planning, ACM Transactions on Software Engineering and Methodology (TOSEM), 27:1, (1-32), Online publication date: 5-Jun-2018.
  4. Mohd Zain M, Kanesan J, Kendall G and Chuah J (2018). Optimization of fed-batch fermentation processes using the Backtracking Search Algorithm, Expert Systems with Applications: An International Journal, 91:C, (286-297), Online publication date: 1-Jan-2018.
  5. Chi C, Zeng W, Oh W, Borson S, Lenskaia T, Shen X and Tonellato P (2017). Personalized long-term prediction of cognitive function, Journal of Biomedical Informatics, 76:C, (78-86), Online publication date: 1-Dec-2017.
  6. Kefalidou G (2017). When immediate interactive feedback boosts optimization problem solving, Computers in Human Behavior, 73:C, (110-124), Online publication date: 1-Aug-2017.
  7. Kroll J, Friboim S and Hemmati H An empirical study of search-based task scheduling in global software development Proceedings of the 39th International Conference on Software Engineering: Software Engineering in Practice Track, (183-192)
  8. ACM
    Hanif S and ud Din S Evaluation of smart scheduling technologies Proceedings of the 31st Annual ACM Symposium on Applied Computing, (2159-2164)
  9. Qu R, Pham N, Bai R and Kendall G (2015). Hybridising heuristics within an estimation distribution algorithm for examination timetabling, Applied Intelligence, 42:4, (679-693), Online publication date: 1-Jun-2015.
  10. Shaker K, Abdullah S, Alqudsi A and Jalab H Hybridizing Meta-heuristics Approaches for Solving University Course Timetabling Problems Proceedings of the 8th International Conference on Rough Sets and Knowledge Technology - Volume 8171, (374-384)
Contributors
  • University of Leicester
  • The University of Nottingham Malaysia Campus

Recommendations

Skip Bibliometrics Section