CRAN

mcga 3.0.3

Machine Coded Genetic Algorithms for Real-Valued Optimization Problems

Released May 13, 2018 by Mehmet Hakan Satman

Dependencies

GA 3.1.1 Rcpp

Machine coded genetic algorithm (MCGA) is a fast tool for real-valued optimization problems. It uses the byte representation of variables rather than real-values. It performs the classical crossover operations (uniform) on these byte representations. Mutation operator is also similar to classical mutation operator, which is to say, it changes a randomly selected byte value of a chromosome by +1 or -1 with probability 1/2. In MCGAs there is no need for encoding-decoding process and the classical operators are directly applicable on real-values. It is fast and can handle a wide range of a search space with high precision. Using a 256-unary alphabet is the main disadvantage of this algorithm but a moderate size population is convenient for many problems. Package also includes multi_mcga function for multi objective optimization problems. This function sorts the chromosomes using their ranks calculated from the non-dominated sorting algorithm.

Source

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Release History

• 3.0.3
• 3.0.1
• 3.0
• 2.0.9
• 2.0.7
• 2.0.6
• 2.0.5
• 2.0.4
• 2.0.3
• 2.0.2
• 2.0.1
• 2.0
• 1.2.1
• 1.2
• 1.1
• 1.0