Abstract:
The Multi Objective Genetic Algorithm is the process of simultaneously optimizing
two or more conflicting objectives subject to certain constraints.
Compute Unified Device Architecture (CUDA) is a general purpose parallel
computing architecture, with a new parallel programming model and instruction
set architecture, which uses the parallel compute engine in NVIDIA GPUs to solve
many complex computational problems in a more efficient way than on a CPU
This thesis considers the development of an Enhanced Multi Objective Genetic
Algorithm (E-MOGA) platform that runs on Compute Unified Device Architecture
(CUDA) hardware as a general purpose tool that can solve conflict optimization
problems. This tool demonstrates significant speed gains using affordable, scalable
and commercially available hardware.
The objectives of the research are: to enhance the general purpose Multi Objective Genetic Algorithm (MOGA) to be ready for execution on CUDA by parallelizing
the time consuming parts, to test the performance of the enhanced MOGA on many
testing cases, to test the quality of the result of the enhanced MOGA and to study
ii
Description:
no of pages 120, 27261, informatics 4/2012 ,in the store
