Function Optimization Using Genetic Algorithm by VHDL

Jay Kumar; Dr. DEEPAK NAGARIA

Function Optimization Using Genetic Algorithm by VHDL

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Function Optimization Using Genetic Algorithm by VHDL

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Abstract

This paper presents the work regarding the synthesis and implementation of a hardware genetic algorithm utilizing very high speed integrated circuit hardware description language (VHDL) for programming FPGAs. Genetic Algorithms were invented to mimic some of the processes observed in natural evolution. The idea with GA is to use this power of evolution to solve optimization problems. They are based on the principles of the evolution via natural selection, employing a population of individuals that undergo selection in the presence of variation-inducing operators such as mutation and recombination (crossover). we solved the problem with the help of hardware description language so it’s take less time to find a result as compare to GA’s because of HDL solve the problem by parallel processing. The present work deals with implementation and optimization of De jong’s first function. Genetic algorithms need large memory banks to store the intermediate results and this has made the hardware implementation of GAs very inefficient but by using FPGA our task become simpler. Field-Programmable Gate Arrays (FPGAs) are flexible circuits that can be easily reconfigured by the designer. The program is written in VHDL and compiled with 32-Bit Microsoft Windows and implemented on a Spartan-3A FPGA from Xilinx.

References

13 Cites in Article

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Jay Kumar, Dr. DEEPAK NAGARIA. 1970. "Function Optimization Using Genetic Algorithm by VHDL". Global Journal of Computer Science and Technology GJCST Volume 10 (GJCST Volume 10 Issue 9).

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GJCST Volume 10 Issue 9

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Submission ReceivedFebruary 13, 2010
Peer Review Double Blind
Handling Editor
Accepted March 2, 2010
Published March 15, 2010

Version of record

v1.2

Issue date

September 25, 2010

Language

English

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Article Matrices

Total Score: 153

Country: Unknown

Subject: Global Journal of Computer Science and Technology

Authors: Jay Kumar, Dr. D.C. DHUBKARYA, Dr. DEEPAK NAGARIA (PhD/Dr. count: 2)

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Publish Date: 2010 03, Mon

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