Abstract. We present an overview over our graph partitioners KaFFPa (Karl-sruhe Fast Flow Partitioner) and KaFFPaE (KaFFPa Evolutionary). KaFFPa is a multilevel graph partitioning algorithm which on the one hand uses novel local improvement algorithms based on max-flow and min-cut computations and more localized FM searches and on the other hand uses more sophisticated global search strategies transferred from multi-grid linear solvers. KaFFPaE is a distributed evolutionary algorithm to solve the Graph Partitioning Problem. KaFFPaE uses KaFFPa and provides new effective crossover and mutation operators. By combining these with a scalable communication protocol we obtain a system that is able to improve the best known partitioning results fo...
The graph partitioning problem is one of the most basic and fundamental problems in theoretical comp...
The realization of efficient parallel graph partitioners requires the parallelization of the multi-l...
Genetic algorithms are stochastic search and optimization techniques which can be used for a wide ra...
We present a novel distributed evolutionary algorithm, KaFFPaE, to solve the Graph Partitioning Prob...
We present a novel distributed evolutionary algorithm, KaFFPaE, to solve the Graph Partitioning Prob...
Abstract. The paper gives an overview of our recent work on balanced graph partitioning – partition ...
Abstract. Parallel graph partitioning is a difficult issue, because the best sequential graph partit...
We describe two different approaches to multi-level graph partitioning (MGP). The first is an approa...
We describe an approach to parallel graph partitioning that scales to hundreds of processors and pro...
Optimal graph partitioning is a foundational problem in computer science, and appears in many differ...
Abstract. The graph-partitioning problem is to divide a graph into several pieces so that the number...
A parallel genetic algorithm for the graph partitioning problem is presented, which combines general...
We present a refinement framework for multilevel hypergraph partitioning that uses max-flow computat...
Graph partitioning divides a graph into several pieces by cutting edges. The graph partitioning prob...
Graph partitioning divides a graph into several pieces by cutting edges. Very effective heuristic pa...
The graph partitioning problem is one of the most basic and fundamental problems in theoretical comp...
The realization of efficient parallel graph partitioners requires the parallelization of the multi-l...
Genetic algorithms are stochastic search and optimization techniques which can be used for a wide ra...
We present a novel distributed evolutionary algorithm, KaFFPaE, to solve the Graph Partitioning Prob...
We present a novel distributed evolutionary algorithm, KaFFPaE, to solve the Graph Partitioning Prob...
Abstract. The paper gives an overview of our recent work on balanced graph partitioning – partition ...
Abstract. Parallel graph partitioning is a difficult issue, because the best sequential graph partit...
We describe two different approaches to multi-level graph partitioning (MGP). The first is an approa...
We describe an approach to parallel graph partitioning that scales to hundreds of processors and pro...
Optimal graph partitioning is a foundational problem in computer science, and appears in many differ...
Abstract. The graph-partitioning problem is to divide a graph into several pieces so that the number...
A parallel genetic algorithm for the graph partitioning problem is presented, which combines general...
We present a refinement framework for multilevel hypergraph partitioning that uses max-flow computat...
Graph partitioning divides a graph into several pieces by cutting edges. The graph partitioning prob...
Graph partitioning divides a graph into several pieces by cutting edges. Very effective heuristic pa...
The graph partitioning problem is one of the most basic and fundamental problems in theoretical comp...
The realization of efficient parallel graph partitioners requires the parallelization of the multi-l...
Genetic algorithms are stochastic search and optimization techniques which can be used for a wide ra...