genericalgorithm.cpp

自己编写的人工智能/遗传算法

// GenericAlgorithm.cpp

#include "stdafx.h"
#include "GenericAlgorithm.h"
#include <time.h>
#include <stdlib.h>
#include <math.h>

namespace 
{
	bool InitializeRandGenerator()
	{
		srand((unsigned int)time(NULL));
		return true;
	}
	
	bool _nomeaning = InitializeRandGenerator();
}

// for common function

double BinaryParse(const BinBits& bits)
{
	int numer = 0;
	int denumer = 1;

	for (int i = SAMPLELENGTH - 1; i >= 0; --i)
	{
		if (bits[i] == '1')
		{
			numer += denumer;
		}
		denumer += denumer;
	}

	return static_cast<double>(numer) / static_cast<double>(denumer);
}


// for MySample

void MySample::Init()
{
	// initialize binary string
	for (size_t i = 0; i < SAMPLELENGTH; ++i)
	{
		if ((rand()%2) == 1)
		{
			_bits[i] = '1';
		}
		else
		{
			_bits[i] = '0';
		}
	}

	// initialize action
	_action = DONE;

}

double MySample::Fitness()
{
	double x = LOWBOUNARY + ZOOMSCALE * BinaryParse(_bits);
	 _fitvalue = x * sin(10.0 * x) +1.0;	
	// _fitvalue = 1 - (x * x) ;	// f(x) = 1 - x^2

	return _fitvalue;
}

void MySample::CalcuPro(double sumfit)
{
	_prop = _fitvalue / sumfit;

	// decide what to do in next step
	if ( static_cast<double>(rand()) / static_cast<double>(RAND_MAX) < _prop )
	{
		_action = STAY;
	}
	else
	{
		_action = CROSSOVER;
	}

}

void MySample::CrossOver(MySample& rhs)
{
	size_t pos = rand() % SAMPLELENGTH;

	// cross over at pos
	MyBit tmp;

	for (size_t i = 0; i < pos; ++i)
	{
		tmp = _bits[i];
		_bits[i] = rhs._bits[i];
		rhs._bits[i] = tmp;
	}
	_action = STAY;
}

void MySample::Mutate()
{
	size_t pos = rand() % SAMPLELENGTH;

	if (_bits[pos] == '1')
	{
		_bits[pos] = '0';
	}
	else
	{
		_bits[pos] = '1';
	}
	_action = STAY;
}