Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
A parallel genetic algorithm for the graph partitioning problem
ICS '91 Proceedings of the 5th international conference on Supercomputing
A multilevel algorithm for partitioning graphs
Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
Fast and effective algorithms for graph partitioning and sparse-matrix ordering
IBM Journal of Research and Development - Special issue: optical lithography I
How Good is Recursive Bisection?
SIAM Journal on Scientific Computing
Multilevel k-way partitioning scheme for irregular graphs
Journal of Parallel and Distributed Computing
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Future Generation Computer Systems - Special issue: Bio-inspired solutions to parallel processing problems
Graph partitioning models for parallel computing
Parallel Computing - Special issue on graph partioning and parallel computing
Parallel optimisation algorithms for multilevel mesh partitioning
Parallel Computing - Special issue on graph partioning and parallel computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Mesh Partitioning: A Multilevel Balancing and Refinement Algorithm
SIAM Journal on Scientific Computing
Genetic Algorithm and Graph Partitioning
IEEE Transactions on Computers
A linear-time heuristic for improving network partitions
DAC '82 Proceedings of the 19th Design Automation Conference
Spectral techniques for graph bisection in genetic algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A PROBE-Based Heuristic for Graph Partitioning
IEEE Transactions on Computers
A new diffusion-based multilevel algorithm for computing graph partitions
Journal of Parallel and Distributed Computing
Engineering algorithms for approximate weighted matching
WEA'07 Proceedings of the 6th international conference on Experimental algorithms
Application of fusion-fission to the multi-way graph partitioning problem
PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
ESA'10 Proceedings of the 18th annual European conference on Algorithms: Part I
An effective multilevel tabu search approach for balanced graph partitioning
Computers and Operations Research
Spectral clustering with more than K eigenvectors
Neurocomputing
Graph partitioning by multi-objective real-valued metaheuristics: A comparative study
Applied Soft Computing
Genetic approaches for graph partitioning: a survey
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Computer Networks: The International Journal of Computer and Telecommunications Networking
A multiagent algorithm for graph partitioning
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Hybrid genetic algorithm within branch-and-cut for the minimum graph bisection problem
EvoCOP'06 Proceedings of the 6th European conference on Evolutionary Computation in Combinatorial Optimization
Engineering graph partitioning algorithms
SEA'12 Proceedings of the 11th international conference on Experimental Algorithms
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The graph-partitioning problem is to divide a graph into several pieces so that the number of vertices in each piece is the same within some defined tolerance and the number of cut edges is minimised. Important applications of the problem arise, for example, in parallel processing where data sets need to be distributed across the memory of a parallel machine. Very effective heuristic algorithms have been developed for this problem which run in real-time, but it is not known how good the partitions are since the problem is, in general, NP-complete. This paper reports an evolutionary search algorithm for finding benchmark partitions. A distinctive feature is the use of a multilevel heuristic algorithm to provide an effective crossover. The technique is tested on several example graphs and it is demonstrated that our method can achieve extremely high quality partitions significantly better than those found by the state-of-the-art graph-partitioning packages.