©1996-2009 All Rights Reserved. Online Journal of
Bioinformatics. You may not store these pages in any form except for your own
personal use. All other usage or distribution is illegal under international
copyright treaties. Permission to use any of these pages in any other way
besides the before mentioned must be gained in writing from the publisher. This
article is exclusively copyrighted in its entirety to OJB publications. This
article may be copied once but may not be reproduced or re-transmitted without
the express permission of the editors.
OJB
Online Journal of
Bioinformaticsâ
Volume 5 : 23-31, 2004
GASP:
a new Genetic Algorithm (based on) Surviving Probability.
A. Carvajal-Rodríguez
Departamento
de Bioquímica, Genética e Inmunología, Facultad de Ciencias Universidad de
Vigo, 36200 VIGO, Spain (acraaj@uvigo.es)
Abstract
.
A. Carvajal-Rodríguez, GASP: a new Genetic Algorithm (based on) Surviving Probability. Online J Bioinformatics 5:23-31, 2004. A new basic genetic algorithm, called GASP
(Genetic Algorithm Surviving Probability) is described. The algorithm differs in some essential properties compared to other genetic algorithms (GA’s) and is more accurate
than traditional GA’s in solving some general problems. In GASP the evolutionary working principle is based in a selection scheme called absolute selection. Effect of the
absolute selection mode is analysed and GASP is compared with the well-known Simple Genetic Algorithm (SGA) via three examples. The third example is a rather novel
application of GAs on a biological problem related with in progress research in conservation genetics. Results show that GASP achieves higher accuracy on reaching the
optimum in the three example problems and is faster than SGA. Data sets, source code and the biological model used in example 3 are available as supplementary
information from http://webs.uvigo.es/c03/webc03/XENETICA/XB2/antonio/GASP/GASP.htm It is proposed that GASP-based GAs may represent a powerful new kind of GAs
for the exploration of many interesting biological problems.
KEY WORDS: Genetic Algorithm, schema theorem, computer simulations in conservation biology problems
©1996-2004 All Rights Reserved. Online Journal of Bioinformatics. You may not store these pages in any form except for your own personal use. All other usage or distribution is illegal under international
copyright treaties. Permission to use any of these pages in any other way besides the before mentioned must be gained in writing from the publisher. This article is exclusively copyrighted in its entirety
to OJB publications. This article may be copied once but may not be reproduced or re-transmitted without the express permission of the editors.