libevocosm-dev 4.0.2-3ubuntu2 / usr / include / libevocosm / scaler.h

/*
    Evocosm is a C++ framework for implementing evolutionary algorithms.

    Copyright 2011 Scott Robert Ladd. All rights reserved.

    Evocosm is user-supported open source software. Its continued development is dependent
    on financial support from the community. You can provide funding by visiting the Evocosm
    website at:

        http://www.coyotegulch.com

    You may license Evocosm in one of two fashions:

    1) Simplified BSD License (FreeBSD License)

    Redistribution and use in source and binary forms, with or without modification, are
    permitted provided that the following conditions are met:

    1.  Redistributions of source code must retain the above copyright notice, this list of
        conditions and the following disclaimer.

    2.  Redistributions in binary form must reproduce the above copyright notice, this list
        of conditions and the following disclaimer in the documentation and/or other materials
        provided with the distribution.

    THIS SOFTWARE IS PROVIDED BY SCOTT ROBERT LADD ``AS IS'' AND ANY EXPRESS OR IMPLIED
    WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
    FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL SCOTT ROBERT LADD OR
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    The views and conclusions contained in the software and documentation are those of the
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    2) Closed-Source Proprietary License

    If your project is a closed-source or proprietary project, the Simplified BSD License may
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    arrange your purchase of an appropriate license.

    The author can be contacted at:

          scott.ladd@coyotegulch.com
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          http:www.coyotegulch.com
*/

#if !defined(LIBEVOCOSM_SCALER_H)
#define LIBEVOCOSM_SCALER_H

// libevocosm
#include "organism.h"
#include "stats.h"

namespace libevocosm
{
    //! Fitness scaling for a population
    /*!
        As a population converges on a definitive solution, the difference
        between fitness values may become very small. That prevents the
        best solutions from having a significant advantage in reproduction.
        Fitness scaling solves this problem by adjusting the fitness values
        to the advantage of the most-fit chromosomes.
        \param OrganismType - The type of organism
    */
    template <class OrganismType>
    class scaler : protected globals
    {
    public:
        //! Virtual destructor
        /*!
            A virtual destructor. By default, it does nothing; this is
            a placeholder that identifies this class as a potential base,
            ensuring that objects of a derived class will have their
            destructors called if they are destroyed through a base-class
            pointer.
        */
        virtual ~scaler()
        {
            // nada
        }

        //! Scale a population's fitness values
        /*!
            The scale_fitness method can adjust the fitness of a population
            to make it more likely that the "best" (whatever that menas)
            organisms have the best chance of reproduction.
            \param a_population - A population of organisms
        */
        virtual void scale_fitness(vector<OrganismType> & a_population) = 0;
    };

    //! A do-nothing scaler
    /*!
        The null_scaler doesn't scale anything; it's just a placeholder used
        in evolutionary algorithms that do not use fitness scaling.
        \param OrganismType - The type of organism
    */
    template <class OrganismType>
    class null_scaler : public scaler<OrganismType>
    {
    public:
        //! Do-nothing scaling function
        /*!
            Has no effect on the target population.
            \param a_population - A population of organisms
        */
        virtual void scale_fitness(vector<OrganismType> & a_population)
        {
            // nada
        }
    };

    //! A linear normalization scaler
    /*!
        A simple scaler implementing a configurable linear normalization scaler, as
        per Goldberg 1979.
        \param OrganismType - The type of organism
    */
    template <class OrganismType>
    class linear_norm_scaler : public scaler<OrganismType>
    {
    public:
        //! Constructor
        /*!
            Creates a new scaler for linear normalization.
        */
        linear_norm_scaler(double a_fitness_multiple = 2.0)
            : m_fitness_multiple(a_fitness_multiple)
        {
            // nada
        }

        //! Scaling function
        /*!
            Performs linear normalization on the fitness of the target population.
            \param a_population - A population of organisms
        */
        virtual void scale_fitness(vector<OrganismType> & a_population)
        {
            // calculate max, average, and minimum fitness for the population
            fitness_stats<OrganismType> stats(a_population);

            // calculate coefficients for fitness scaling
            double slope;
            double intercept;
            double delta;

            if (stats.getMin() > ((m_fitness_multiple * stats.getMean() - stats.getMax()) / (m_fitness_multiple - 1.0)))
            {
                // normal scaling
                delta = stats.getMax() - stats.getMean();
                slope = (m_fitness_multiple - 1.0) * stats.getMean() / delta;
                intercept = stats.getMean() * (stats.getMax() - m_fitness_multiple * stats.getMean()) / delta;
            }
            else
            {
                // extreme scaling
                delta = stats.getMean() - stats.getMin();
                slope = stats.getMean() / delta;
                intercept = -stats.getMin() * stats.getMean() / delta;
            }

            // adjust fitness values
            for (int n = 0; n < a_population.size(); ++n)
                a_population[n].fitness = slope * a_population[n].fitness + intercept;
        }

    private:
        double m_fitness_multiple;
    };

    //! A windowed fitness scaler
    /*!
        Implements windowed fitness scaling, whereby all fitness values are modified
        by subtracting the minimum fitness in the population.
        \param OrganismType - The type of organism
    */
    template <class OrganismType>
    class windowed_scaler : public scaler<OrganismType>
    {
    public:
        //! Constructor
        /*!
            Creates a new windowed scaler with a given set of parameters.
        */
        windowed_scaler()
        {
            // nada
        }

        //! Scaling function
        /*!
            Performs windowed scaling on the fitness of the target population.
            \param a_population - A population of organisms
        */
        virtual void scale_fitness(vector<OrganismType> & a_population)
        {
            fitness_stats<OrganismType> stats(a_population);

            // assign new fitness values
            for (int n = 0; n < a_population.size(); ++n)
                a_population[n].fitness = stats.getMin();
        }
    };

    //! An exponential fitness scaler
    /*!
        Implements an exponential fitness scaling, whereby all fitness values are modified
        such that new fitness = (a * fitness + b) ^ n.
        \param OrganismType - The type of organism
    */
    template <class OrganismType>
    class exponential_scaler : public scaler<OrganismType>
    {
    public:
        //! Constructor
        /*!
            Creates a new exponential scaler with a given set of parameters. The
            formula used is new_fitness = (a * fitness + b) ^ power.
            \param a_a - A multplier against the fitness
            \param a_b - Added to fitness before exponentiation
            \param a_power - Power applied to the value
        */
        exponential_scaler(double a_a = 1.0, double a_b = 1.0, double a_power = 2.0)
          : m_a(a_a),
            m_b(a_b),
            m_power(a_power)
        {
            // nada
        }

        //! Scaling function
        /*!
            Performs exponential scaling on the fitness of the target population.
            \param a_population - A population of organisms
        */
        virtual void scale_fitness(vector<OrganismType> & a_population)
        {
            // assign new fitness values
            for (int n = 0; n < a_population.size(); ++n)
                a_population[n].fitness = pow((m_a * a_population[n].fitness + m_b),m_power);
        }

    private:
        double m_a;
        double m_b;
        double m_power;
    };

    //! A quadratic scaler
    /*!
        Uses a quadratic equation to scale the fitness of organisms.
        \param OrganismType - The type of organism
    */
    template <class OrganismType>
    class quadratic_scaler : public scaler<OrganismType>
    {
    public:
        //! Constructor
        /*!
            Creates a new scaler for quadratic scaling.
        */
        quadratic_scaler(double a_a, double a_b, double a_c)
            : m_a(a_a), m_b(a_b), m_c(a_c)
        {
            // nada
        }

        //! Scaling function
        /*!
            Performs quadratic scling on the fitness of the target population.
            \param a_population - A population of organisms
        */
        virtual void scale_fitness(vector<OrganismType> & a_population)
        {
            // adjust fitness values
            for (int n = 0; n < a_population.size(); ++n)
            {
                double f = a_population[n].fitness;
                a_population[n].fitness = m_a * pow(f,2.0) + m_b * f + m_c;
            }
        }

    private:
        double m_a;
        double m_b;
        double m_c;
    };

    //! A sigma scaler
    /*!
        A sigma scaler, as per Forrest and Tanese.
        \param OrganismType - The type of organism
    */
    template <class OrganismType>
    class sigma_scaler : public scaler<OrganismType>
    {
    public:
        //! Constructor
        /*!
            Creates a new sigma scaler
        */
        sigma_scaler()
        {
        }

        //! Scaling function
        /*!
            Performs sigma scaling, which maintains selection pressure over the
            length of a run, thus minimizing the affects of convergence on
            reproductive selection. The function adjusts an organism's fitness
            in relation to the standard deviation of the population's fitness.
            \param a_population - A population of organisms
        */
        virtual void scale_fitness(vector<OrganismType> & a_population)
        {
            fitness_stats<OrganismType> stats(a_population);

            // calculate 2 times the std. deviation (sigma)
            double sigma2 = 2.0 * stats.getSigma();

            // now assign new fitness values
            if (sigma2 == 0.0)
            {
                for (int n = 0; n < a_population.size(); ++n)
                    a_population[n].fitness = 1.0;
            }
            else
            {
                for (int n = 0; n < a_population.size(); ++n)
                {
                    // change fitness
                    a_population[n].fitness = (1.0 + a_population[n].fitness / stats.mean) / sigma2;

                    // avoid tiny or zero fitness value; everyone gets to reproduce
                    if (a_population[n].fitness < 0.1)
                        a_population[n].fitness = 0.1;
                }
            }
        }
    };

};

#endif