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British Journal of Mathematics & Computer Science

British Journal of Mathematics & Computer Science, ISSN: 2231-0851,Vol.: 20, Issue.: 2

Original-research-article

mcga: R Implementation of the Machine-coded Genetic Algorithms

 

Mehmet Hakan Satman1* and Emre Akadal2
1Department of Econometrics, Istanbul University, Istanbul, Turkey.
2Department of Informatics, Istanbul University, Istanbul, Turkey.

 

Article Information
Editor(s):
(1) Doina Bein, Applied Research Laboratory, The Pennsylvania State University, USA.
Reviewers:
(1) Omar Farouq Lutfy, University of Technology, Baghdad, Iraq.
(2) C. M. Wankhade, Mumbai University, Navi Mumbai, India.
Complete Peer review History: http://www.sciencedomain.org/review-history/17397

 

Abstracts

 

Genetic Algorithms (GAs) are global optimization and search algorithms that mimic the natural selection and genetic processes. Floating-point GAs (FPGAs) are other type of GAs which directly operate on real-vectors without requiring a geno-type pheno-type distinction and encoding-decoding processes. However, the classical crossover and mutation operators are not directly applicable on the real-vectors. As a result of this, new types of genetic operators are developed for FPGAs. Machine-coded GAs (MCGAs) apply byte-based genetic operators on the byte representations of the candidate solutions. This natural encoding scheme makes classical crossover operators applicable on the real-vectors. Addition to this, MCGAs report more precise results in larger domains of decision variables. Mutation operation on the byte representations of variables have also similar e ects with its binary counterpart. The R package mcga defines plug-in versions of byte-based operators that can be integrated with the recently developed function ga in package GA. Low level utility functions are written in C++ and wrapped with the Rcpp and .Call interface of R. Advantages and disadvantages of using byte-based operators are discussed and demonstrated on some univariate and multivariate optimization problems.

 

Keywords :

Genetic algorithms; real-valued optimization; R.

 

Full Article - PDF    Page 1-20    Article Metrics

 

DOI : 10.9734/BJMCS/2017/30894

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