<%@ Page Language="VB" AutoEventWireup="false" CodeFile="GeneticAlg.aspx.vb" Inherits="GeneticAlg" %> Genetic Algorithm

Example 1: The Assignment Problem Using the Genetic Algorithm


The genetic algorithm is a heuristic method of finding approximate solutions to optimization problems.  This algorithm incorporates the evolutionary
theory of the survival of the fittest, along with crossover and mutation, to create successive generations of individuals that evolve to a better solution.

Below is an assignment problem.  Each task must be performed by one person, A to M.  The table below shows how well each person performs
each task.  Find the person-job assignment that produces the best score.  Note that in practice, this problem should be solved using linear programming!

The variables that may affect the outcome of the algorithm are the initial population size and the mutation rate.  You can adjust these variables and see
how the outcome is affected.  You can also set the termination criteria.  Change the number of iterations the algorithm will do after a change in the
incumbent, average fitness or fitness of the worst solution to see how the final result and the algorithm's speed  are affected.  The best
possible result for this problem is 323.

Set the values of the variables below then press the Start button.  This will bring you to page displaying a graph of the results.  The blue line
represents the fitness of the incumbent.  The green line represents the average fitness of the current population.  The red line represents the lowest
fitness in the current population.  To return from this page, press the back button in your browser.  To re-run the algorithm with the same
parameters, refresh the page.

1 2345 678910 A3431 20272424 18333519 B1414 22342619 22292219 C2216 21273525 30222323 D1721 24163122 20272617 E1729 22311819 26242514 F2629 37343720 21252727 G3028 37282923 19333021 H2821 30243520 24243224 I1918 19282827 26322322 J3022 29193029 29212018 K2925 35292718 30281923 L1519 19332224 25313321 M2732 27292921 19252027
Population: * Mutation Rate: %* Termination (iterations after last change): *  


To obtain the code for the used to solve this problem, click on the links below:
Genetic Algorithm Code
Graphing Code