Machine Coded Genetic Algorithms for Real-Valued Optimization Problems
Performs arithmetic crossover operation on a pair of two selected pare...
Performs blx (blend) crossover operation on a pair of two selected par...
Performs crossover operation on a pair of two selected parent candidat...
Performs one-point crossover operation on a pair of two selected paren...
Performs two-point crossover operation on a pair of two selected paren...
Performs mutation operation on a given double vector
Performs mutation operation on a given double vector using dynamic mut...
Performs mutation operation on a given double vector
Performs mutation operation on a given double vector with dynamic muta...
Mutation operator for byte representation of double values
Mutation operator for byte representation of double values
Mutation operator for byte representation of double values
Converting sizeof(double) bytes to a double value
Converting p * sizeof(double) bytes to a vector of p double values
Byte representation of a double typed variable
Byte representation of a vector of double typed variables
Altering vector of doubles to satisfy boundary constraints
Performs flat crossover operation on a pair of two selected parent can...
Performs linear crossover operation on a pair of two selected parent c...
Maximum value of a double typed variable
Internal mcga objects
Machine Coded Genetic Algorithms for Real-valued Optimization Problems
Performs machine coded genetic algorithms on a function subject to be ...
Performs a machine-coded genetic algorithm search for a given optimiza...
Performs multi objective machine coded genetic algorithms.
One Point Crossover operation on the two vectors of bytes
One-point Crossover operation on the two vectors of doubles using thei...
Performs sbx (simulated binary) crossover operation on a pair of two s...
Byte-length of a double typed variable
Byte-length of a int typed variable
Byte-length of a long typed variable
Two Point Crossover operation on the two vectors of bytes
Two-point Crossover operation on the two vectors of doubles using thei...
Performs unfair average crossover operation on a pair of two selected ...
Uniform Crossover operation on the two vectors of bytes
Uniform Crossover operation on the two vectors of doubles using their ...
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.