Result: Multi Population Pattern Searching Algorithm: A New Evolutionary Method Based on the Idea of Messy Genetic Algorithm

Title:
Multi Population Pattern Searching Algorithm: A New Evolutionary Method Based on the Idea of Messy Genetic Algorithm
Authors:
KWASNICKA, Halina, PRZEWOZNICZEK, Michal
Source:
IEEE transactions on evolutionary computation. 15(5):715-734
Publisher Information:
New York, NY: Institute of Electrical and Electronics Engineers, 2011.
Publication Year:
2011
Physical Description:
print, 20 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Algorithme génétique, Genetic algorithm, Algoritmo genético, Algorithme recherche, Search algorithm, Algoritmo búsqueda, Algorithme évolutionniste, Evolutionary algorithm, Algoritmo evoluciónista, Attention sélective, Selective attention, Atención selectiva, Codage binaire, Binary coding, Codificación binaria, Efficacité, Efficiency, Eficacia, Epistasie, Epistasis, Epistasia, Estimation Bayes, Bayes estimation, Estimación Bayes, Goulot étranglement, Bottleneck, Gollete estrangulamiento, Optimisation, Optimization, Optimización, Bayesian optimization algorithm (BOA), deceptive functions, evolutionary algorithms, gene patterns, linkage learning, messy GA
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Wroclaw University of Technology, Wroclaw 50-370, Poland
ISSN:
1089-778X
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=24624809
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.24624809
Database:
PASCAL Archive

Further Information

One of the main evolutionary algorithms bottlenecks is the significant effectiveness dropdown caused by increasing number of genes necessary for coding the problem solution. In this paper, we present a multi population pattern searching algorithm (MuPPetS), which is supposed to be an answer to situations where long coded individuals are a must. MuPPetS uses some of the messy GA ideas like coding and operators. The presented algorithm uses the binary coding, however the objective is to use MuPPetS against real-life problems, whatever coding schema. The main novelty in the proposed algorithm is a gene pattern idea based on retrieving, and using knowledge of gene groups which contains genes highly dependent on each other. Thanks to gene patterns the effectiveness of data exchange between population individuals improves, and the algorithm gains new, interesting, and beneficial features like a kind of selective attention effect.