UniformCrossOverOnDoublesUsingBytes

A vector of doubles of the first parent

A vector of doubles of the second parent

This function is a C++ wrapper for crossing-over of two double vectors of candidate solutions using their byte representations

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.

Mehmet Satman

mcga

Machine Coded Genetic Algorithms for Real-Valued Optimization
Problems

UniformCrossOverOnDoublesUsingBytes function

<dl><dt>d1</dt>
<dd>A vector of doubles of the first parent</dd>
<dt>d2</dt>
<dd>A vector of doubles of the second parent</dd></dl>

Arguments

Mehmet Hakan Satman - mhsatman@istanbul.edu.tr

Author

Uniform Crossover operation on the two vectors of doubles using their byte representations — UniformCrossOverOnDoublesUsingBytes

<dl>

<dt>d1</dt>
<dd>A vector of doubles of the first parent</dd>


<dt>d2</dt>
<dd>A vector of doubles of the second parent</dd>

</dl>

UniformCrossOverOnDoublesUsingBytes: Uniform Crossover operation on the two vectors of doubles using their byte representations

Description

Usage

Value

Arguments

Author

See Also

Examples