R package mcga: Machine Coded Genetic Algorithms for Real-Valued Optimization Problems. 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.
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References in zbMATH (referenced in 2 articles )
Showing results 1 to 2 of 2.
- Katharine Mullen: Continuous Global Optimization in R (2014) not zbMATH
- Luca Scrucca: GA: A Package for Genetic Algorithms in R (2013) not zbMATH