An Efficient Distributed Genetic Algorithm Architecture for Vector Quantizer Design
Wen-Jyi Hwang1, Chien-Min Ou *, 2, Peng-Chieh Hung1, Cheng-Yen Yang 1, Tun-Hao Yu 1
Identifiers and Pagination:Year: 2010
First Page: 20
Last Page: 29
Publisher Id: TOAIJ-4-20
Article History:Electronic publication date: 18/2/2010
Collection year: 2010
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
This paper presents a novel distributed genetic algorithm (GA) architecture for the design of vector quantizers. The design is based on a multi-core architecture, where each island of the GA is associated with a hardware accelerator and a softcore processor for independent genetic evolutions. An on-chip RAM with a mutex circuit is adopted for the migration of genetic strings among different islands. This allows a simple and flexible migration for the implementation of hardware distributed GA. Experimental results shows that the proposed architecture has significantly lower computational time as compared with its software counterparts running on multicore processors with multithreading for GA-based optimization.