Changes between Version 7 and Version 8 of Features/Genetic Programming


Ignore:
Timestamp:
01/06/12 00:13:20 (8 years ago)
Author:
swagner
Comment:

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  • Features/Genetic Programming

    v7 v8  
    33[[http://dev.heuristiclab.com/trac/hl/core/wiki/UsersSamples#GeneticProgramming | Samples for Genetic Programming]]
    44
    5 HeuristicLab supports tree-based (Koza-style) genetic programming. This classical form of genetic programming uses a tree data structure as representation of solution candidates. The data types and operators to work with tree-based solution candidates are implemented in the plugin `HeuristicLab.Encodings.SymbolicExpressionTree`.
     5!HeuristicLab supports tree-based (Koza-style) genetic programming. This classical form of genetic programming uses a tree data structure as representation of solution candidates. The data types and operators to work with tree-based solution candidates are implemented in the plugin `HeuristicLab.Encodings.SymbolicExpressionTree`.
    66
    77Supported operators (in 3.3.6):
     
    2525 * Artificial Ant problem (`HeuristicLab.Problems.ArtificialAnt`)
    2626
    27 Samples for all three of these problems are available on the `start page` in HeuristicLab. Additional samples are available here: [[http://dev.heuristiclab.com/trac/hl/core/wiki/UsersSamples#GeneticProgramming]]
     27Samples for all three of these problems are available on the `start page` in !HeuristicLab. Additional samples are available here: [[http://dev.heuristiclab.com/trac/hl/core/wiki/UsersSamples#GeneticProgramming]]
    2828
    2929== Symbolic regression ==
     
    3232The symbols that can be used in symbolic regression models is problem dependent. So these symbols (mathematical operators: +,-,/,* ... and operands: constants, variables) are implemented in the problem specific plugin `HeuristicLab.Problems.DataAnalysis.Symbolic`. Evaluators for these problem-specific instances of symbolic expression trees are also implemented in this plugin.
    3333
    34 HeuristicLab provides many possibilities to analyse the resulting symbolic regression solutions. Some of them are shown in the screenshot.
     34!HeuristicLab provides many possibilities to analyse the resulting symbolic regression solutions. Some of them are shown in the screenshot.
    3535
    3636Symbolic regression features:
     
    7171
    7272== Algorithms for Genetic Programming ==
    73 Because of the strict separation of the implementations of algorithms, problems, and encodings in HeuristicLab. It is possible to solve symbolic regression of symbolic classification problems with all population based algorithms using either a crossover or manipulation operator to evolve solutions. The algorithm that comes closest to the original formulation of genetic programming by Koza is the genetic algorithm.
     73Because of the strict separation of the implementations of algorithms, problems, and encodings in !HeuristicLab. It is possible to solve symbolic regression of symbolic classification problems with all population based algorithms using either a crossover or manipulation operator to evolve solutions. The algorithm that comes closest to the original formulation of genetic programming by Koza is the genetic algorithm.
    7474
    7575Available algorithms for genetic programming: