Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Computers and Operations Research
P-Complete Approximation Problems
Journal of the ACM (JACM)
Rank-Two Relaxation Heuristics for MAX-CUT and Other Binary Quadratic Programs
SIAM Journal on Optimization
A Spectral Bundle Method for Semidefinite Programming
SIAM Journal on Optimization
A Taxonomy of Hybrid Metaheuristics
Journal of Heuristics
A linear-time heuristic for improving network partitions
DAC '82 Proceedings of the 19th Design Automation Conference
Computing minimum cuts by randomized search heuristics
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A modified VNS metaheuristic for max-bisection problems
Journal of Computational and Applied Mathematics
Local search starting from an LP solution: Fast and quite good
Journal of Experimental Algorithmics (JEA)
Ant colony optimization and the minimum cut problem
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A hybridization between memetic algorithm and semidefinite relaxation for the max-cut problem
Proceedings of the 14th annual conference on Genetic and evolutionary computation
A new discrete filled function method for solving large scale max-cut problems
Numerical Algorithms
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The Max-Cut problem consists of finding a partition of the graph nodes into two subsets, such that the sum of the edge weights having endpoints in different subsets is maximized. This NP-hard problem for non planar graphs has different applications in areas such as VLSI and ASIC design. This paper proposes an evolutionary hybrid algorithm based on low-level hybridization between Memetic Algorithms and Variable Neighborhood Search. This algorithm is tested and compared with the results, found in the bibliography, obtained by other hybrid metaheuristics for the same problem. Achieved experimental results show the suitability of the approach, and that the proposed hybrid evolutionary algorithm finds near-optimal solutions. Moreover, on a set of standard test problems, new best known solutions were produced for several instances.