Journal of the ACM (JACM)
Stochastic evolution: a fast effective heuristic for some generic layout problems
DAC '90 Proceedings of the 27th ACM/IEEE Design Automation Conference
A new simultaneous circuit partitioning and chip placement approach based on simulated annealing
DAC '90 Proceedings of the 27th ACM/IEEE Design Automation Conference
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Finding good approximate vertex and edge partitions is NP-hard
Information Processing Letters
Advanced query optimization techniques for relational database systems
Advanced query optimization techniques for relational database systems
Recent directions in netlist partitioning: a survey
Integration, the VLSI Journal
An introduction to the analysis of algorithms
An introduction to the analysis of algorithms
A probability-based approach to VLSI circuit partitioning
DAC '96 Proceedings of the 33rd annual Design Automation Conference
A timing driven N-way chip and multi-chip partitioner
ICCAD '93 Proceedings of the 1993 IEEE/ACM international conference on Computer-aided design
Greedy, Prohibition, and Reactive Heuristics for Graph Partitioning
IEEE Transactions on Computers
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Improved Large-Step Markov Chain Variants for the Symmetric TSP
Journal of Heuristics
Genetic Algorithm and Graph Partitioning
IEEE Transactions on Computers
HPCN Europe 1996 Proceedings of the International Conference and Exhibition on High-Performance Computing and Networking
A linear-time heuristic for improving network partitions
DAC '82 Proceedings of the 19th Design Automation Conference
Fitness Landscapes, Memetic Algorithms, and Greedy Operators for Graph Bipartitioning
Evolutionary Computation
Approximating game-theoretic optimal strategies for full-scale poker
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
On implementation choices for iterative improvement partitioning algorithms
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Investigation of the Fitness Landscapes in Graph Bipartitioning: An Empirical Study
Journal of Heuristics
Multi-attractor gene reordering for graph bisection
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Geometric crossover for multiway graph partitioning
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Spectral techniques for graph bisection in genetic algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Geometric crossovers for multiway graph partitioning
Evolutionary Computation
An Enzyme-Inspired Approach to Surmount Barriers in Graph Bisection
ICCSA '08 Proceeding sof the international conference on Computational Science and Its Applications, Part I
Genetic approaches for graph partitioning: a survey
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A Note on Edge-based Graph Partitioning and its Linear Algebraic Structure
Journal of Mathematical Modelling and Algorithms
A spanning tree-based encoding of the MAX CUT problem for evolutionary search
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
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We propose a new heuristic for the graph partitioning problem. Based on the traditional iterative improvement framework, the heuristic uses a new type of gain in selecting vertices to move between partitions. The new type of gain provides a good explanation for the performance difference of tie-breaking strategies in KL-based iterative improvement graph partitioning algorithms. The new heuristic performed excellently. Theoretical arguments supporting its efficacy are also provided. As the proposed heuristic is considered a good candidate for local optimization engines in metaheuristics, we combined it with a genetic algorithm as a sample case and obtained a surprising result that even the average results over 1,000 runs equalled the best known for most graphs.