jburkardt

SIMPLE_GA\ A Simple Genetic Algorithm {#simple_ga-a-simple-genetic-algorithm align=”center”} ==========================

SIMPLE_GA is a C++ program which implements a simple genetic algorithm, by Dennis Cormier and Sita Raghavan.

Here, we consider the task of constrained optimization of a scalar function. That is, we have a function F(X), where X is an M-vector satisfying simple constraints for each component I:

        X_MIN[I] <= X[I] <= X_MAX[I]

and, subject to those constraints, we seek a vector X which maximizes the value F(X).

In the example given here, the spatial dimension M is 3, the function F(X) is X[1]\^2 - X[1]*X[2] + X[3], and the constraints are

        0 <= X[1] <= 5
        0 <= X[2] <= 5
       -2 <= X[3] <= 2

The correct solution is [5,0,2].

Licensing: {#licensing align=”center”}

The computer code and data files made available on this web page are distributed under the GNU LGPL license.

Languages: {#languages align=”center”}

SIMPLE_GA is available in a C++ version.

Related Programs and Data: {#related-programs-and-data align=”center”}

IMAGE_MATCH_GENETIC, a MATLAB program which tries to match a 256x256 JPEG image by blending 32 colored rectangles, using ideas from genetic algorithms, based on an example by Nick Berry.

Author: {#author align=”center”}

Original version by Dennis Cormier, Sita Raghavan. This C++ version by John Burkardt.

Reference: {#reference align=”center”}

1. Zbigniew Michalewicz,\ Genetic Algorithms + Data Structures = Evolution Programs,\ Third Edition,\ Springer, 1996,\ ISBN: 3-540-60676-9,\ LC: QA76.618.M53.

Source Code: {#source-code align=”center”}

• simple_ga.cpp, the source code.

Examples and Tests: {#examples-and-tests align=”center”}

• simple_ga_input.txt, an input file.
• simple_ga_output.txt, the output file.

List of Routines: {#list-of-routines align=”center”}

• MAIN supervises the genetic algorithm.
• CROSSOVER selects two parents for the single point crossover.
• ELITIST stores the best member of the previous generation.
• EVALUATE implements the user-defined valuation function
• I4_UNIFORM_AB returns a scaled pseudorandom I4 between A and B.
• INITIALIZE initializes the genes within the variables bounds.
• KEEP_THE_BEST keeps track of the best member of the population.
• MUTATE performs a random uniform mutation.
• R8_UNIFORM_AB returns a scaled pseudorandom R8.
• REPORT reports progress of the simulation.
• SELECTOR is the selection function.
• TIMESTAMP prints the current YMDHMS date as a time stamp.
• XOVER performs crossover of the two selected parents.

You can go up one level to the C++ source codes.

Last revised on 28 April 2014.