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This repository contains implementation of both serial & parallel version of an Evolutionary Algorithm for Large-Scale Black Box Optimization

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souradipp76/Parallel_LSBBO_Algorithm

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Parallel_LSBBO_Algorithm

The aim of this project is to implement both serial and parallel version of an Evolutionary Algorithm for Large-Scale Black Box Optimization. The parallelization is done using OpenMP.

Description

In this project, the proposed evolutionary strategy algorithm using sparse plus low rank model for large-scale black box optimization was implemented and was parallelized using the OpenMP API. The proposed algorithm consists of two methods - (i) Rank-One evolution strategy(R1-ES) which uses a single principle direction for optimization and its extended version (ii) Rank-m evolution strategy(Rm-ES) which uses multiple principle directions. Both of these algorithms were parallelized and their convergence and speedup were analysed w.r.t some critical parameters.

Prerequisites

Original Paper

Paper

Final Report & Results

Report

Development and contribution

Contributions in shape of [Pull Requests] are always welcome.

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This repository contains implementation of both serial & parallel version of an Evolutionary Algorithm for Large-Scale Black Box Optimization

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