Experiments in quadratic 0-1 programming
Mathematical Programming: Series A and B
A branch and bound algorithm for the maximum clique problem
Computers and Operations Research
Adaptive Memory Tabu Search for Binary Quadratic Programs
Management Science
Solving quadratic (0,1)-problems by semidefinite programs and cutting planes
Mathematical Programming: Series A and B
New ideas in optimization
Memetic algorithms: a short introduction
New ideas in optimization
Fitness landscapes and memetic algorithm design
New ideas in optimization
A scatter search approach to unconstrained quadratic binary programs
New ideas in optimization
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Fitness Landscapes, Memetic Algorithms, and Greedy Operators for Graph Bipartitioning
Evolutionary Computation
Solving the maximum clique problem by k-opt local search
Proceedings of the 2004 ACM symposium on Applied computing
Advanced fitness landscape analysis and the performance of memetic algorithms
Evolutionary Computation - Special issue on magnetic algorithms
An effective local search for the maximum clique problem
Information Processing Letters
Information-theoretic inference of large transcriptional regulatory networks
EURASIP Journal on Bioinformatics and Systems Biology
A linearization framework for unconstrained quadratic (0-1) problems
Discrete Applied Mathematics
Canonical Dual Approach to Binary Factor Analysis
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Genetic Local Search for Optimum Multiuser Detection Problem in DS-CDMA Systems
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
An effective local search for the maximum clique problem
Information Processing Letters
Biological network inference using redundancy analysis
BIRD'07 Proceedings of the 1st international conference on Bioinformatics research and development
Expert Systems with Applications: An International Journal
Efficient evaluations for solving large 0-1 unconstrained quadratic optimisation problems
International Journal of Metaheuristics
NK landscapes, problem difficulty, and hybrid evolutionary algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation
An adaptive local search based genetic algorithm for solving multi-objective facility layout problem
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Expert Systems with Applications: An International Journal
Advanced neighborhoods and problem difficulty measures
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Landscape properties and hybrid evolutionary algorithm for optimum multiuser detection problem
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
Constructing uniform designs: A heuristic integer programming method
Journal of Complexity
Particle Algorithms for Optimization on Binary Spaces
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special Issue on Monte Carlo Methods in Statistics
Metaheuristics for robust graph coloring
Journal of Heuristics
Elementary landscape decomposition of the 0-1 unconstrained quadratic optimization
Journal of Heuristics
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In this paper, a greedy heuristic and two local search algorithms, 1-opt local search and k-opt local search, are proposed for the unconstrained binary quadratic programming problem (BQP). These heuristics are well suited for the incorporation into meta-heuristics such as evolutionary algorithms. Their performance is compared for 115 problem instances. All methods are capable of producing high quality solutions in short time. In particular, the greedy heuristic is able to find near optimum solutions a few percent below the best-known solutions, and the local search procedures are sufficient to find the best-known solutions of all problem instances with n ≤ 100. The k-opt local searches even find the best-known solutions for all problems of size n ≤ 250 and for 11 out of 15 instances of size n = 500 in all runs. For larger problems (n = 500, 1000, 2500), the heuristics appear to be capable of finding near optimum solutions quickly. Therefore, the proposed heuristics—especially the k-opt local search—offer a great potential for the incorporation in more sophisticated meta-heuristics.