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EvolutionaryLearning Class
Neural networks' evolutionary learning algorithm, which is based on Genetic Algorithms.
Inheritance Hierarchy
SystemObject
  Accord.Neuro.LearningEvolutionaryLearning

Namespace:  Accord.Neuro.Learning
Assembly:  Accord.Neuro (in Accord.Neuro.dll) Version: 3.8.0
Syntax
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public class EvolutionaryLearning : ISupervisedLearning
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The EvolutionaryLearning type exposes the following members.

Constructors
Methods
  NameDescription
Public methodEquals
Determines whether the specified object is equal to the current object.
(Inherited from Object.)
Protected methodFinalize
Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
(Inherited from Object.)
Public methodGetHashCode
Serves as the default hash function.
(Inherited from Object.)
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Protected methodMemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.)
Public methodRun
Runs learning iteration.
Public methodRunEpoch
Runs learning epoch.
Public methodToString
Returns a string that represents the current object.
(Inherited from Object.)
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Extension Methods
  NameDescription
Public Extension MethodHasMethod
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.)
Public Extension MethodIsEqual
Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices.
(Defined by Matrix.)
Public Extension MethodTo(Type)Overloaded.
Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.)
Public Extension MethodToTOverloaded.
Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.)
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Remarks

The class implements supervised neural network's learning algorithm, which is based on Genetic Algorithms. For the given neural network, it create a population of DoubleArrayChromosome chromosomes, which represent neural network's weights. Then, during the learning process, the genetic population evolves and weights, which are represented by the best chromosome, are set to the source neural network.

See Population class for additional information about genetic population and evolutionary based search.

Sample usage (training network to calculate XOR function):

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// initialize input and output values
double[][] input = new double[4][] {
    new double[] {-1,  1}, new double[] {-1, 1},
    new double[] { 1, -1}, new double[] { 1, 1}
};
double[][] output = new double[4][] {
    new double[] {-1}, new double[] { 1},
    new double[] { 1}, new double[] {-1}
};
// create neural network
ActivationNetwork   network = new ActivationNetwork(
    BipolarSigmoidFunction( 2 ),
    2, // two inputs in the network
    2, // two neurons in the first layer
    1 ); // one neuron in the second layer
// create teacher
EvolutionaryLearning teacher = new EvolutionaryLearning( network,
    100 ); // number of chromosomes in genetic population
// loop
while ( !needToStop )
{
    // run epoch of learning procedure
    double error = teacher.RunEpoch( input, output );
    // check error value to see if we need to stop
    // ...
}
See Also