unfair_average_crossover

This function is not called directly but is given as a parameter in <code>GA::ga</code> function. 
In <code>GA::ga</code>, if the parameter <code>crossover=</code> is set to <code>unfair_average_crossover</code> than
the unfair average crossover operator is applied in the genetic search.

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

unfair_average_crossover function

<dl><dt>object</dt>
<dd>A <code>GA::ga</code> object</dd>
<dt>parents</dt>
<dd>Indices of the selected parents</dd>
<dt>...</dt>
<dd>Additional arguments to be passed to the function</dd></dl>

Arguments

Mehmet Hakan Satman - mhsatman@istanbul.edu.tr

Author

Performs unfair average crossover operation on a pair of two selected parent candidate solutions — unfair_average_crossover

<dl>

<dt>object</dt>
<dd>A <code>GA::ga</code> object</dd>


<dt>parents</dt>
<dd>Indices of the selected parents</dd>


<dt>...</dt>
<dd>Additional arguments to be passed to the function</dd>

</dl>

unfair_average_crossover: Performs unfair average crossover operation on a pair of two selected parent candidate solutions

Description

Usage

Value

Arguments

Author

Examples