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


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
(1) Doina Bein, Applied Research Laboratory, The Pennsylvania State University, USA.
(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


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.

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DOI : 10.9734/BJMCS/2017/30894

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