Changes between Version 1 and Version 2 of Documentation/Howto/Implement Genetic Programming Problems

Timestamp:

04/06/12 18:58:22 (12 years ago)

Author:

gkronber

Comment:

added images

Documentation/Howto/Implement Genetic Programming Problems

@@ -2 +2 @@
 = How-to Implement Custom Genetic Programming Problems =
 
-This how-to describes how you can implement your own plug-in to solve custom problems with genetic programming. GP is a very general evolutionary problem solving method and can be used to find solutions for a large number of different problems. The default installation of !HeuristicLab includes plug-ins for solving symbolic regression (and classification) problems and the artificial ant problems with genetic programming. However, if you want to use GP to solve other kinds of problems it is necessary to write a little bit of code for your own problem plug-in. This can be a little intriguing at first because the HeuristicLab source code is already very extensive and especially the implementation of the symbolic data analysis features includes so many extensions that it cannot be really used as a template for custom implementations. The source code for the artificial ant problem is much easier to understand and includes everything that is necessary for custom implementations of problems for genetic programming. 
+This how-to describes how you can implement your own plug-in to solve custom problems with genetic programming. GP is a very general evolutionary problem solving method and can be used to find solutions for a large number of different problems. The default installation of !HeuristicLab includes plug-ins for solving symbolic regression (and classification) problems and the artificial ant problems with genetic programming. However, if you want to use GP to solve other kinds of problems it is necessary to write a little bit of code for your own problem plug-in. This can be a little intriguing at first because the !HeuristicLab source code is already very extensive and especially the implementation of the symbolic data analysis features includes so many extensions that it cannot be really used as a template for custom implementations. The source code for the artificial ant problem is much easier to understand and includes everything that is necessary for custom implementations of problems for genetic programming. 
 
 
@@ -15 +15 @@
 The lawn is a rectangular of size (n x m), which is 'connected' at the ends so that the mower appears on the left side if it drops off of the side (toroidal shape). This is  The mower should mow each cell of the lawn. The mower can mow a cell twice but this has no effect.
 
-[image(lawnmower.jpg)]
+[[Image(lawn_mower.png)]]
 
 == Design ==
@@ -894 +894 @@
 }}}
 
+[[Image(lawn mower problem parameters.png)]]
+
 == Extension of the Implementation == 
 
@@ -1142 +1144 @@
 }}}
 
+[[Image(lawn mower program view.png)]]
+
 {{{SolutionLawnView.cs}}}
 {{{
@@ -1221 +1225 @@
 }}}
 
+[[Image(lawn mower solution view.png)]]
+
 [Koza 1994] J. Koza. Genetic Programming II: Automatic Discovery of Reusable Programs. Cambridge, MA, The MIT Press. 1994