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Evolving Combinatorial Problem Instances That Are Difficult to Solve

2006
*
Evolutionary Computation
*

In this paper we demonstrate how evolutionary computation can be used

doi:10.1162/evco.2006.14.4.433
pmid:17109606
fatcat:togz2qqffrcptjkiwwbke42vl4
*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. ...##
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Evolving heuristics with genetic programming

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

doi:10.1145/1389095.1389212
dblp:conf/gecco/Bader-El-DenP08
fatcat:gsjk6hcs4jb3zcphc53ffyi6cm
*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. ...##
###
An Analysis of Problem Difficulty for a Class of Optimisation Heuristics
[chapter]

2007
*
Lecture Notes in Computer Science
*

This implies

doi:10.1007/978-3-540-71615-0_18
fatcat:3astp6yktzejxodcrfjuixytfq
*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. ...##
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Evolving difficult SAT instances thanks to local search
[article]

2010
*
arXiv
*
pre-print

We propose

arXiv:1011.5866v1
fatcat:cntv4gpvkngenkavqqxqk4vcom
*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. ...##
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Editorial for the Special Issue on Combinatorial Optimization Problems

2016
*
Evolutionary Computation
*

In particular, great research efforts have been devoted

doi:10.1162/evco_e_00192
pmid:27906599
fatcat:qmptzsack5b3pi3p33iopu5s3m
*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*. ...##
###
A Comparative Study of Meta-heuristic Algorithms for Solving Quadratic Assignment Problem

2014
*
International Journal of Advanced Computer Science and Applications
*

Quadratic Assignment

doi:10.14569/ijacsa.2014.050101
fatcat:mmsh5mv2crde3i6a3e25nvahlu
*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. ...##
###
Determining Whether a Problem Characteristic Affects Heuristic Performance
[chapter]

2008
*
Studies in Computational Intelligence
*

This implies

doi:10.1007/978-3-540-70807-0_2
fatcat:uvijx5mgxrd2plv6zswawn5hn4
*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. ...##
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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
*

The contribution of this paper is

doi:10.1145/1389095.1389335
dblp:conf/gecco/KumarJBR08
fatcat:tqreiwdnxfgxrcxrak4xazir5e
*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*. ...##
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Learning hardware using multiple-valued logic - Part 1: introduction and approach

2002
*
IEEE Micro
*

Hanyu et al. describe a robot vision coprocessor

doi:10.1109/mm.2002.1013303
fatcat:fjeo52iks5hafg5gcuz53h7sxy
*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 ...##
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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
*

., OPL (Optimization Programming Language),

doi:10.1109/icstw.2011.20
dblp:conf/icst/Lazaar11
fatcat:s2pprm7ht5abfiamqu2it43hki
*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 ...##
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Analysis and extension of the Inc* on the satisfiability testing problem

2008
*
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)
*

Rather than attempting

doi:10.1109/cec.2008.4631250
dblp:conf/cec/Bader-El-DenP08
fatcat:52vamwyabnc2ndiwmarutodb5m
*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*. ...##
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Evolving heuristically difficult instances of combinatorial problems

2009
*
Proceedings of the 11th Annual conference on Genetic and evolutionary computation - GECCO '09
*

An evolutionary algorithm can search a space of

doi:10.1145/1569901.1569941
dblp:conf/gecco/Julstrom09
fatcat:tkjwcysavza7vklzsu3ln63gnm
*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. ...##
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Evidence of an exponential speed-up in the solution of hard optimization problems
[article]

2017
*
arXiv
*
pre-print

Often, these

arXiv:1710.09278v1
fatcat:qkbwar3xejgedmiobavii4mlt4
*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. ...##
###
Review on the Methods to Solve Combinatorial Optimization Problems Particularly: Quadratic Assignment Model

2018
*
International Journal of Engineering & Technology
*

Those databases

doi:10.14419/ijet.v7i3.20.18722
fatcat:5b7dfvvdtfe7bgcypah74xtngm
*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 ...##
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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
*

Such "adaptive algorithms" have

doi:10.1145/2739482.2768459
dblp:conf/gecco/Ryser-WelchMA15
fatcat:5wwicbbh6zaqlguz564jd77sdq
*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. ...
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