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Evolving Combinatorial Problem Instances That Are Difficult to Solve

Jano I. van Hemert
2006 Evolutionary Computation  
In this paper we demonstrate how evolutionary computation can be used to acquire difficult to solve combinatorial problem instances, thereby stress-testing the corresponding algorithms used to solve these  ...  Problem instances acquired through this technique are more difficult than ones found in popular benchmarks.  ...  The author wishes to thank Conor Ryan at the University of Limerick, IE, for making available their computing facilities.  ... 
doi:10.1162/evco.2006.14.4.433 pmid:17109606 fatcat:togz2qqffrcptjkiwwbke42vl4

Evolving heuristics with genetic programming

Mohamed Bahy Bader-El-Den, Riccardo Poli
2008 Proceedings of the 10th annual conference on Genetic and evolutionary computation - GECCO '08  
Tests against well-known local search heuristics on a variety of benchmark problems reveal that the evolved heuristics are superior.  ...  Hyper-Heuristics are methods to choose and combine heuristics to generate new ones. In this work, we use a grammar-based genetic programming system as a Hyper-Heuristic framework.  ...  Rather than attempting to directly solve a difficult problem, the algorithm dynamically chooses a simplified instance of the problem, and tries to solve it.  ... 
doi:10.1145/1389095.1389212 dblp:conf/gecco/Bader-El-DenP08 fatcat:gsjk6hcs4jb3zcphc53ffyi6cm

An Analysis of Problem Difficulty for a Class of Optimisation Heuristics [chapter]

Enda Ridge, Daniel Kudenko
2007 Lecture Notes in Computer Science  
This implies that for ACO, it is insufficient to report results on problems classified only by problem size, as has been commonly done in most ACO research to date.  ...  This paper investigates the effect of the cost matrix standard deviation of Travelling Salesman Problem (TSP) instances on the performance of a class of combinatorial optimisation heuristics.  ...  Acknowledgements The authors are grateful to the reviewers for the helpful and encouraging comments and criticisms.  ... 
doi:10.1007/978-3-540-71615-0_18 fatcat:3astp6yktzejxodcrfjuixytfq

Evolving difficult SAT instances thanks to local search [article]

Olivier Bailleux
2010 arXiv   pre-print
We propose to use local search algorithms to produce SAT instances which are harder to solve than randomly generated k-CNF formulae.  ...  The first results, obtained with rudimentary search algorithms, show that the approach deserves further study.  ...  Evolving combinatorial problem instances that are difficult to solve. Evolutionary Computation, 14(4), 2006.  ... 
arXiv:1011.5866v1 fatcat:cntv4gpvkngenkavqqxqk4vcom

Editorial for the Special Issue on Combinatorial Optimization Problems

Francisco Chicano, Christian Blum, Gabriela Ochoa
2016 Evolutionary Computation  
In particular, great research efforts have been devoted to the development and application of metaheuristic algorithms to solve combinatorial optimization problems.  ...  Their contents, outlined in the next paragraphs, reflect the diversity of the application domains and the methods applied to solve the problems.  ...  Most of the metaheuristic algorithms that are applied to hard combinatorial optimization problems use the objective function as the only source of information of the problem.  ... 
doi:10.1162/evco_e_00192 pmid:27906599 fatcat:qmptzsack5b3pi3p33iopu5s3m

A Comparative Study of Meta-heuristic Algorithms for Solving Quadratic Assignment Problem

Gamal Abd, Abeer M., El-Sayed M.
2014 International Journal of Advanced Computer Science and Applications  
Quadratic Assignment Problem (QAP) is an NP-hard combinatorial optimization problem, therefore, solving the QAP requires applying one or more of the meta-heuristic algorithms.  ...  The results show that Genetic Algorithm has a better solution quality while Tabu Search has a faster execution time in comparison with other Meta-heuristic algorithms for solving QAP.  ...  There are multiple methods used to solve optimization problems of both the mathematical and combinatorial types.  ... 
doi:10.14569/ijacsa.2014.050101 fatcat:mmsh5mv2crde3i6a3e25nvahlu

Determining Whether a Problem Characteristic Affects Heuristic Performance [chapter]

Enda Ridge, Daniel Kudenko
2008 Studies in Computational Intelligence  
This implies that for ACO, it is insufficient to report results on problems classified only by problem size, as has been commonly done in most ACO research to date.  ...  Results demonstrate that for a given instance size, an increase in the standard deviation of the cost matrix of instances results in an increase in the difficulty of the instances.  ...  Most recently, Van Hemert [27] evolved problem instances of a fixed size that were difficult to solve for two heuristics: Chained Lin-Kernighan and Lin Kernighan with Cluster Compensation.  ... 
doi:10.1007/978-3-540-70807-0_2 fatcat:uvijx5mgxrd2plv6zswawn5hn4

Evolution of hyperheuristics for the biobjective 0/1 knapsack problem by multiobjective genetic programming

Rajeev Kumar, Ashwin H. Joshi, Krishna K. Banka, Peter I. Rockett
2008 Proceedings of the 10th annual conference on Genetic and evolutionary computation - GECCO '08  
The contribution of this paper is to show that a genetic programming system can evolve a set of heuristics that can give solutions on the Pareto front for multiobjective combinatorial problems.  ...  The 0/1 knapsack problem is one of the most exhaustively studied NP-hard combinatorial optimization problems.  ...  A major advantage of this approach is that we need not solve every new instance of the problemthe evolved hyperheuristics can be straightforwardly reused to solve new problem instances.  ... 
doi:10.1145/1389095.1389335 dblp:conf/gecco/KumarJBR08 fatcat:tqreiwdnxfgxrcxrak4xazir5e

Learning hardware using multiple-valued logic - Part 1: introduction and approach

M. Perkowski, D. Foote, Qihong Chen, A. Al-Rabadi, L. Jozwiak
2002 IEEE Micro  
Hanyu et al. describe a robot vision coprocessor that performs matching by solving the NPhard maximum-clique problem. 4 Software solutions are adequate for many other NP-hard problems in computer vision  ...  In different ways, these approaches try to solve complex and poorly defined problems that previously developed analytic models could not efficiently tackle.  ...  But these optimization problems are more difficult because they not only check that the formula is satisfied, but must also find the best method to satisfy the formula-by, for instance, assigning a minimum  ... 
doi:10.1109/mm.2002.1013303 fatcat:fjeo52iks5hafg5gcuz53h7sxy

CPTEST: A Framework for the Automatic Fault Detection, Localization and Correction of Constraint Programs

Nadjib Lazaar
2011 2011 IEEE Fourth International Conference on Software Testing, Verification and Validation Workshops  
., OPL (Optimization Programming Language), are more and more used in businesscritical programs.  ...  As any other critical programs, they require to be thoroughly tested and corrected to prevent catastrophic loss of money.  ...  INTRODUCTION Constraint Programming emerged since 1960's for solving difficult combinatorial problems [7] and evolved through the development of high-level modeling languages such as OPL (Optimization  ... 
doi:10.1109/icstw.2011.20 dblp:conf/icst/Lazaar11 fatcat:s2pprm7ht5abfiamqu2it43hki

Analysis and extension of the Inc* on the satisfiability testing problem

Mohamed Bader-El-Den, Riccardo Poli
2008 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)  
Rather than attempting to directly solve a difficult problem, the algorithm dynamically chooses a smaller instance of the problem, and then increases the size of the instance only after the previous simplified  ...  instances have been solved, until the full size of the problem is reached.  ...  Rather than attempting to directly solve a difficult problem, let us first derive a sequence of progressively simpler and simpler instances of the problem.  ... 
doi:10.1109/cec.2008.4631250 dblp:conf/cec/Bader-El-DenP08 fatcat:52vamwyabnc2ndiwmarutodb5m

Evolving heuristically difficult instances of combinatorial problems

Bryant A. Julstrom
2009 Proceedings of the 11th Annual conference on Genetic and evolutionary computation - GECCO '09  
An evolutionary algorithm can search a space of problem instances for cases that are heuristically difficult.  ...  As an example, a genetic algorithm searches for instances of the quadratic knapsack problem that are difficult for a straightforward greedy heuristic.  ...  Here, however, we seek to generate instances of a combinatorial problem that are difficult for a heuristic.  ... 
doi:10.1145/1569901.1569941 dblp:conf/gecco/Julstrom09 fatcat:tkjwcysavza7vklzsu3ln63gnm

Evidence of an exponential speed-up in the solution of hard optimization problems [article]

Fabio L. Traversa, Pietro Cicotti, Forrest Sheldon, Massimiliano Di Ventra
2017 arXiv   pre-print
Often, these problems are particularly difficult to solve because they belong to the NP-hard class, namely algorithms that always find a solution in polynomial time are not known.  ...  Here, we show a non-combinatorial approach to hard optimization problems that achieves an exponential speed-up and finds better approximations than the current state-of-the-art.  ...  Often, these problems are particularly difficult to solve because they belong to the NP-hard class, namely algorithms that always find a solution in polynomial time are not known.  ... 
arXiv:1710.09278v1 fatcat:qkbwar3xejgedmiobavii4mlt4

Review on the Methods to Solve Combinatorial Optimization Problems Particularly: Quadratic Assignment Model

Asaad Shakir Hameed, Burhanuddin Mohd Aboobaider, Ngo Hea Choon, Modhi Lafta Mutar, Wassim Habib Bilal
2018 International Journal of Engineering & Technology  
Those databases are regarded extensive adequate in covering QAP and the methods utilized in solving QAP.  ...  The quadratic assignment problem (QAP) be appropriate to the group of NP-hard issues and is measured as a challenging problem of the combinatorial optimization.  ...  Acknowledgement The authors would like to thanks to Faculty of Information and Communication Technology, Centre for Research and Innovation Management, Universiti Teknikal Malaysia Melaka for providing  ... 
doi:10.14419/ijet.v7i3.20.18722 fatcat:5b7dfvvdtfe7bgcypah74xtngm

Generating Human-readable Algorithms for the Travelling Salesman Problem using Hyper-Heuristics

Patricia Ryser-Welch, Julian F. Miller, Shahriar Asta
2015 Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference - GECCO Companion '15  
Such "adaptive algorithms" have solved several well-established combinatorial problems, with a high level of generality.  ...  However, the evolved sequences of heuristic operations are often very long and defy human comprehension.  ...  This paper is concerned with the evolution of metaheuristics, to solve instances of the Travelling Salesman Problem. This hard combinatorial problem is a well-established testbed for new algorithms.  ... 
doi:10.1145/2739482.2768459 dblp:conf/gecco/Ryser-WelchMA15 fatcat:5wwicbbh6zaqlguz564jd77sdq
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