jcgp.backend.modules.problem

Class DigitalCircuitProblem

java.lang.Object

jcgp.backend.modules.Module

jcgp.backend.modules.problem.Problem

jcgp.backend.modules.problem.TestCaseProblem<UnsignedInteger>

jcgp.backend.modules.problem.DigitalCircuitProblem

public class DigitalCircuitProblem
extends TestCaseProblem<UnsignedInteger>

Digital circuit problem

Using this problem type, digital logic circuits can be evolved. parseData() must be used to load the desired circuit truth table in the standard CGP .plu format.

Author:

Eduardo Pedroni

See Also:

DigitalCircuitFunctions

Nested Class Summary

Nested classes/interfaces inherited from class jcgp.backend.modules.problem.TestCaseProblem

TestCaseProblem.TestCase<U>

Constructor Summary

Constructors

Constructor and Description
DigitalCircuitProblem(Resources resources) Construct a new instance of DigitalCircuitProblem.

Method Summary

Modifier and Type	Method and Description
void	evaluate(Population population, Resources resources) The most important method of the problem type.
int	hasPerfectSolution(Population population) Used to assert whether a given population contains a perfect solution to the problem.
TestCaseProblem.TestCase<UnsignedInteger>	parseTestCase(java.lang.String[] inputs, java.lang.String[] outputs) This method is used internally by addTestCase() in order to appropriately parse strings into the right data type for the test cases.

Methods inherited from class jcgp.backend.modules.problem.TestCaseProblem

addTestCase, clearTestCases, getTestCases, hasImprovement, parseProblemData

Methods inherited from class jcgp.backend.modules.problem.Problem

getBestFitness, getFileExtension, getFitnessOrientation, getFunctionSet, reset

Methods inherited from class jcgp.backend.modules.Module

getLocalParameters, toString

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

DigitalCircuitProblem

public DigitalCircuitProblem(Resources resources)

Construct a new instance of DigitalCircuitProblem.

Parameters:

resources - a reference to the experiment's resources.

Method Detail

evaluate

public void evaluate(Population population,
                     Resources resources)

Description copied from class: Problem

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.

Specified by:

evaluate in class Problem

Parameters:

population - the population to be evaluated.

resources - parameters and utilities for optional reference.

parseTestCase

public TestCaseProblem.TestCase<UnsignedInteger> parseTestCase(java.lang.String[] inputs,
                                                               java.lang.String[] outputs)

Description copied from class: TestCaseProblem

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.

Parameters:

inputs - the inputs represented as strings.

outputs - the outputs represented as strings.

Returns:

the parsed test case.

hasPerfectSolution

public int hasPerfectSolution(Population population)

Description copied from class: Problem

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.

Specified by:

hasPerfectSolution in class Problem

Parameters:

population - the population to search through for a perfect chromosome.

Returns:

the perfect solution index, if one exits, -1 if no perfect solution was found.