Evolutionary optimization of NP-hard problem instances

Evolutionary optimization of NP-hard problem instances

Justin Shenk

Justin Shenk

Osnabrück, Lower Saxony

A genetic algorithm approach to graph partitioning

Artificial Intelligence

Description

Genetic algorithms can be used for optimizing parameters of a graph partition and allow informed problem-specific mutation functions. NP-hard problems like 3SAT provide opportunity for optimizing genetic algorithms by generating random problems which can be used for feature discovery of graph partitioning solutions. Using this model, the probability of hardness of 3SAT can be defined by selecting problems for phase transition topography. This project aims to use Intel Math Kernel Library for Python and Numpy implementations benchmarked against native Python and Numpy for performance comparison.

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Justin S. added photos to project Evolutionary optimization of NP-hard problem instances

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Evolutionary optimization of NP-hard problem instances

Genetic algorithms can be used for optimizing parameters of a graph partition and allow informed problem-specific mutation functions. NP-hard problems like 3SAT provide opportunity for optimizing genetic algorithms by generating random problems which can be used for feature discovery of graph partitioning solutions. Using this model, the probability of hardness of 3SAT can be defined by selecting problems for phase transition topography. This project aims to use Intel Math Kernel Library for Python and Numpy implementations benchmarked against native Python and Numpy for performance comparison.

Medium 0 ptobiuaxtrcxigrmg1clrztxtxfvijr1qe9kct7ch3a0sajx9xczv9ff3pg0sarybkckb9mftlc0d2lrlou4qbakllcxd22ypouvtu xk01ndugzpmwnbmcwpt

Justin S. created project Evolutionary optimization of NP-hard problem instances

Medium cbf1371b 3053 4731 ae43 b2e83c908eb4

Evolutionary optimization of NP-hard problem instances

Genetic algorithms can be used for optimizing parameters of a graph partition and allow informed problem-specific mutation functions. NP-hard problems like 3SAT provide opportunity for optimizing genetic algorithms by generating random problems which can be used for feature discovery of graph partitioning solutions. Using this model, the probability of hardness of 3SAT can be defined by selecting problems for phase transition topography. This project aims to use Intel Math Kernel Library for Python and Numpy implementations benchmarked against native Python and Numpy for performance comparison.

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