From 4f26361eb08aa09334d1eea0a124110c20d809ce Mon Sep 17 00:00:00 2001 From: Eduardo Pedroni Date: Sat, 18 Oct 2014 15:21:45 +0200 Subject: Moved javadocs to a different repository for hosting, updated README --- .../modules/problem/SymbolicRegressionProblem.html | 436 --------------------- 1 file changed, 436 deletions(-) delete mode 100644 doc/jcgp/backend/modules/problem/SymbolicRegressionProblem.html (limited to 'doc/jcgp/backend/modules/problem/SymbolicRegressionProblem.html') diff --git a/doc/jcgp/backend/modules/problem/SymbolicRegressionProblem.html b/doc/jcgp/backend/modules/problem/SymbolicRegressionProblem.html deleted file mode 100644 index 7515c68..0000000 --- a/doc/jcgp/backend/modules/problem/SymbolicRegressionProblem.html +++ /dev/null @@ -1,436 +0,0 @@ - - - - - -SymbolicRegressionProblem - - - - - - - - -
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jcgp.backend.modules.problem
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Class SymbolicRegressionProblem

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    public class SymbolicRegressionProblem
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    Symbolic regression functions -

    - Using this problem type, regression problems can be solved. - parseData() must be used to load the desired function - data in the standard CGP .dat format. -

    - This problem uses quite a few parameters: -
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    • Error threshold: the maximum difference allowed between an - evolved output and the equivalent output from the problem data. - Outputs within the error threshold will be considered correct. - This is only used if hits is enabled.
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    • Perfection threshold: if the fitness is calculated without - using the hits method, it is a decimal value. A solution is - considered perfect when the difference between its fitness and - the maximum possible fitness is within the perfection threshold.
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    • Hits-based fitness: increment the fitness by 1 whenever the - chromosome output is within the error threshold.
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    Author:
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    Eduardo Pedroni
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    See Also:
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    SymbolicRegressionFunctions
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      Constructor Detail

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        SymbolicRegressionProblem

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        public SymbolicRegressionProblem(Resources resources)
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        Creates a new instance of SymbolicRegressionProblem.
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        Parameters:
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        resources - a reference to the experiment's resources.
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      Method Detail

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        evaluate

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        public void evaluate(Population population)
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        Description copied from class: Problem
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        The most important method of the problem type. This is called once - per generation, when the new population has been generated. -

        - The basic functionality of this method is to loop through all chromosomes - in the population and decode them according to the problem type. The - fitness of each chromosome is then calculated using the problem data - or otherwise (subjective problem types such as art generation might - leave fitness evaluations up to the user) and assigned to the appropriate - chromosome. -

        - In addition, realisations of this method should update the value of - bestFitness as appropriate, since the value of this parameter is displayed - if a GUI is in use.
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        Specified by:
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        evaluate in class Problem
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        population - the population to be evaluated.
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        parseTestCase

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        public TestCaseProblem.TestCase<java.lang.Double> parseTestCase(java.lang.String[] inputs,
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        Description copied from class: TestCaseProblem
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        This method is used internally by addTestCase() in order - to appropriately parse strings into the right data type for the - test cases. Since the data type is problem-dependent, subclasses must - implement this method. This method must return a built TestCase - object from the arguments given.
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        Parameters:
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        inputs - the inputs represented as strings.
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        outputs - the outputs represented as strings.
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        the parsed test case.
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        hasPerfectSolution

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        public int hasPerfectSolution(Population population)
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        Description copied from class: Problem
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        Used to assert whether a given population contains a perfect solution - to the problem. It is up to the problem to define what qualifies - a perfect solution, as some problems (subject ones such as music and - art evolution, for example) might not have perfect solutions at all.
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        Specified by:
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        hasPerfectSolution in class Problem
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        Parameters:
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        population - the population to search through for a perfect chromosome.
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        Returns:
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        the perfect solution index, if one exits, -1 if no perfect solution was found.
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