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Proceedings ArticleDOI

Grammatical rules for the automated construction of heuristics

18 Jul 2010-pp 1-8

TL;DR: This paper presents a three-steps methodology that combines multiple sequence alignment and grammatical induction in order to automatically generate high performing solving strategies for a combinatorial optimisation problem.

AbstractDeveloping a problem-domain independent methodology to automatically generate high performing solving strategies for specific problems is one of the challenging trends on hyper-heuristics design. Designing hyper-heuristics is important because they raise the level of generality on automated problem solving by attempting to select and/or generate tailored heuristics for the problem at hand. In this paper, we present a three-steps methodology that combines multiple sequence alignment and grammatical induction in order to automatically generate high performing solving strategies for a combinatorial optimisation problem. We present proof-of-concept results of applying this methodology to instances of the well-known symmetric TSP. The goal here is to demonstrate feasibility rather than compete with state of the art TSP solvers. This TSP is chosen only because it is an easy to state and well known problem.

Topics: Heuristics (58%)

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Citations
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01 Jan 2005
TL;DR: Several variants and generalizations of the Or-opt heuristic for the Symmetric Travelling Salesman Problem are developed and compared on random and planar instances to significantly improve upon the standard 2-opt and Or- opt heuristics.
Abstract: Several variants and generalizations of the Or-opt heuristic for the Symmetric Travelling Salesman Problem are developed and compared on random and planar instances. Some of the proposed algorithms are shown to significantly improve upon the standard 2-opt and Or-opt heuristics.

6 citations


References
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Book
01 Jan 1994
TL;DR: A Case Study: TSPs in Printed Circuit Board Production and Practical TSP Solving.
Abstract: Basic Concepts.- Related Problems and Applications.- Geometric Concepts.- Candidate Sets.- Construction Heuristics.- Improving Solutions.- Heuristics for Large Geometric Problems.- Further Heuristic Approaches.- Lower Bounds.- A Case Study: TSPs in Printed Circuit Board Production.- Practical TSP Solving.

776 citations


"Grammatical rules for the automated..." refers methods in this paper

  • ...In the pattern-based heuristics generation, an input dataset is employed to train randomly generated sequences of low-level heuristics (high-level heuristics)....

    [...]


Book
16 Jan 1997
TL;DR: This chapter discusses the construction of phylogenetic trees, a type of tree-building based on DNA assembly, and its applications in medicine, dentistry, and neuroscience.
Abstract: Preface 1. Basic Concepts of Molecular Biology 2. Strings, Graphs, and Algorithms 3. Sequence Comparison and Database Search 4. Fragment Assembly of DNA 5. Physical Mapping of DNA 6. Phylogenetic Trees 7. Genome Rearrangements 8. Molecular Structure Prediction 9. Epilogue: Computing with DNA Answers to Selected Exercises / References / Index

737 citations


Journal ArticleDOI
TL;DR: This paper reviews some works on the application of MAs to well-known combinatorial optimization problems, and places them in a framework defined by a general syntactic model, which provides them with a classification scheme based on a computable index D, which facilitates algorithmic comparisons and suggests areas for future research.
Abstract: The combination of evolutionary algorithms with local search was named "memetic algorithms" (MAs) (Moscato, 1989). These methods are inspired by models of natural systems that combine the evolutionary adaptation of a population with individual learning within the lifetimes of its members. Additionally, MAs are inspired by Richard Dawkin's concept of a meme, which represents a unit of cultural evolution that can exhibit local refinement (Dawkins, 1976). In the case of MA's, "memes" refer to the strategies (e.g., local refinement, perturbation, or constructive methods, etc.) that are employed to improve individuals. In this paper, we review some works on the application of MAs to well-known combinatorial optimization problems, and place them in a framework defined by a general syntactic model. This model provides us with a classification scheme based on a computable index D, which facilitates algorithmic comparisons and suggests areas for future research. Also, by having an abstract model for this class of metaheuristics, it is possible to explore their design space and better understand their behavior from a theoretical standpoint. We illustrate the theoretical and practical relevance of this model and taxonomy for MAs in the context of a discussion of important design issues that must be addressed to produce effective and efficient MAs.

692 citations


"Grammatical rules for the automated..." refers background in this paper

  • ...…of Computer Science, University of Nottingham, UK, email:gzt@cs.nott.ac.uk Natalio Krasnogor, ASAP Group, School of Computer Science, University of Nottingham, UK, email:nxk@cs.nott.ac.uk presented and discussed in Section V. Finally, conclusions and further work are the subject of Section VI....

    [...]


Book
30 Oct 2013
TL;DR: 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.
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

565 citations


"Grammatical rules for the automated..." refers background in this paper

  • ...…of Computer Science, University of Nottingham, UK, email:gzt@cs.nott.ac.uk Natalio Krasnogor, ASAP Group, School of Computer Science, University of Nottingham, UK, email:nxk@cs.nott.ac.uk presented and discussed in Section V. Finally, conclusions and further work are the subject of Section VI....

    [...]


Book ChapterDOI
27 Sep 1998
TL;DR: This paper investigates the usefulness of a new operator, inver-over, for an evolutionary algorithm for the TSP, and the proposed operator is unary, since the inversion is applied to a segment of a single individual, however, the selection of a segment to be inverted is population driven, thus the operator displays some characterictics of recombination.
Abstract: In this paper we investigate the usefulness of a new operator, inver-over, for an evolutionary algorithm for the TSP. Inver-over is based on simple inversion, however, knowledge taken from other individuals in the population influences its action. Thus, on one hand, the proposed operator is unary, since the inversion is applied to a segment of a single individual, however, the selection of a segment to be inverted is population driven, thus the operator displays some characterictics of recombination.

195 citations


"Grammatical rules for the automated..." refers methods in this paper

  • ...In the pattern-based heuristics generation, an input dataset is employed to train randomly generated sequences of low-level heuristics (high-level heuristics)....

    [...]