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Evolving Combinatorial Problem Instances That Are Difficult to Solve
2006
Evolutionary Computation
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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
2008
Proceedings of the 10th annual conference on Genetic and evolutionary computation - GECCO '08
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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]
2007
Lecture Notes in Computer Science
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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]
2010
arXiv
pre-print
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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
2016
Evolutionary Computation
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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
2014
International Journal of Advanced Computer Science and Applications
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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]
2008
Studies in Computational Intelligence
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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
2008
Proceedings of the 10th annual conference on Genetic and evolutionary computation - GECCO '08
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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
2002
IEEE Micro
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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
2011
2011 IEEE Fourth International Conference on Software Testing, Verification and Validation Workshops
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., 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
2008
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)
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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
2009
Proceedings of the 11th Annual conference on Genetic and evolutionary computation - GECCO '09
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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]
2017
arXiv
pre-print
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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
2018
International Journal of Engineering & Technology
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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
2015
Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference - GECCO Companion '15
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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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