Intel Distribution For Python To Create A Genetic Algorithm

Genetic algorithms (GA) simulate natural selection to solve finite and unconstrained optimization problems. Traditional methods take time and resources to address NP-hard optimization problems

Selection, crossover, and mutation are three crucial biology-inspired procedures that may be used to provide a high-quality output for GAs

It’s critical to specify the chromosomal representation and the GA procedures before applying GAs to a particular issue

Biological crossover is the same procedure as this one. In this case, more than one parent is chosen, and the genetic material of the parents is used to make one or more children

A novel answer may be obtained by a little, haphazard modification to the chromosome. It is often administered with little probability and is used to preserve and add variation to the genetic population

With libraries like Intel oneAPI Data Analytics Library (oneDAL) and Intel oneAPI Math Kernel Library (oneMKL), developers may use Intel Distribution for Python to obtain near-native code performance

Use the Data Parallel Extension for Numba (numba-dpex) range kernel to optimize the genetic algorithm using the Intel Distribution for Python