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
    CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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    The views and conclusions contained in the software and documentation are those of the
    authors and should not be interpreted as representing official policies, either expressed
    or implied, of Scott Robert Ladd.

    2) Closed-Source Proprietary License

    If your project is a closed-source or proprietary project, the Simplified BSD License may
    not be appropriate or desirable. In such cases, contact the Evocosm copyright holder to
    arrange your purchase of an appropriate license.

    The author can be contacted at:

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

#if !defined(LIBEVOCOSM_SCALER_H)
#define LIBEVOCOSM_SCALER_H

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

namespace libevocosm
{

    template <class OrganismType>
    class scaler : protected globals
    {
    public:

        virtual ~scaler()
        {
            // nada
        }


        virtual void scale_fitness(vector<OrganismType> & a_population) = 0;
    };


    template <class OrganismType>
    class null_scaler : public scaler<OrganismType>
    {
    public:

        virtual void scale_fitness(vector<OrganismType> & a_population)
        {
            // nada
        }
    };


    template <class OrganismType>
    class linear_norm_scaler : public scaler<OrganismType>
    {
    public:

        linear_norm_scaler(double a_fitness_multiple = 2.0)
            : m_fitness_multiple(a_fitness_multiple)
        {
            // nada
        }


        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;
    };


    template <class OrganismType>
    class windowed_scaler : public scaler<OrganismType>
    {
    public:

        windowed_scaler()
        {
            // nada
        }


        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();
        }
    };


    template <class OrganismType>
    class exponential_scaler : public scaler<OrganismType>
    {
    public:

        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
        }


        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;
    };


    template <class OrganismType>
    class quadratic_scaler : public scaler<OrganismType>
    {
    public:

        quadratic_scaler(double a_a, double a_b, double a_c)
            : m_a(a_a), m_b(a_b), m_c(a_c)
        {
            // nada
        }


        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;
    };


    template <class OrganismType>
    class sigma_scaler : public scaler<OrganismType>
    {
    public:

        sigma_scaler()
        {
        }


        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