scalation.minima

GeneticAlgorithm

class GeneticAlgorithm extends AnyRef

This class performs local search to find minima of functions defined on integer vector domains (z^n).

minimize f(x) subject to g(x) <= 0, x in Z^n

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  1. GeneticAlgorithm
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Instance Constructors

  1. new GeneticAlgorithm(f: (VectorI) ⇒ Double, x0: VectorI, vMax: Int = 100, g: (VectorI) ⇒ Double = null, maxStep: Int = 5)

    f

    the objective function to be minimize (f maps an integer vector to a double)

    x0

    the starting point for the search (seed for GA)

    g

    the constraint function to be satisfied, if any

    maxStep

    the maximum/starting step size (make larger for larger domains)

Type Members

  1. type Vec_Func = (VectorI, Double)

    Pair consisting of an integer vector and its functional value (a double)

Value Members

  1. final def !=(arg0: AnyRef): Boolean

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  2. final def !=(arg0: Any): Boolean

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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  5. final def ==(arg0: Any): Boolean

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  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

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  8. def crossOver: Unit

    For each individual in the population, cross it with some other individual.

  9. final def eq(arg0: AnyRef): Boolean

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  10. def equals(arg0: Any): Boolean

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  11. def fg(x: VectorI): Double

    The objective function f re-scaled by a weighted penalty, if constrained.

    The objective function f re-scaled by a weighted penalty, if constrained.

    x

    the coordinate values of the currrent point

  12. def finalize(): Unit

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  13. def fitnessCrossOver: Unit

    For each individual in the population, cross it with some other individual.

    For each individual in the population, cross it with some other individual. Let the crossover be dependent of the fitness of the individual.

  14. def fittest: (VectorI, Double)

    Find the fittest individual (smallest value of objective function).

  15. def genPopulation: Unit

    Generate an initial population of individuals.

  16. final def getClass(): java.lang.Class[_]

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  17. def hashCode(): Int

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  18. final def isInstanceOf[T0]: Boolean

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  19. def mutate: Unit

    Randomly select individuals for mutation (change a value at one position).

  20. final def ne(arg0: AnyRef): Boolean

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  21. final def notify(): Unit

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  22. final def notifyAll(): Unit

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  23. def printPopulation: Unit

    Print the current population

  24. def solve: (VectorI, Double)

    Solve the minimization problem using a genetic algorithm.

  25. final def synchronized[T0](arg0: ⇒ T0): T0

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  26. def toString(): String

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  27. final def wait(): Unit

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  28. final def wait(arg0: Long, arg1: Int): Unit

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  29. final def wait(arg0: Long): Unit

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