swaits/differential-evolution
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use normal distribution on initial random individual creation

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initial default range tweaked way down
rename "POP" template parameter to "POPSIZE"
added RunOneGeneration() method - generations can now be run one at a time
added SetRange(i,min,max) method
added a decent amount of comments through main algorithm and class declaration
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swaits committed 2007-05-25 01:24:26 +00:00
1 parent c194ae5fc5
commit 6f03c0b106
5 files changed
+267 -113
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de.h
+39 -29
View File
@@ -8,9 +8,8 @@
namespace DE
{
template <unsigned int DIM, unsigned int POP=(DIM*10)>
template <unsigned int DIM, unsigned int POPSIZE=(DIM*10)>
class Engine
{
public:
@@ -18,58 +17,69 @@ namespace DE
Engine();
virtual ~Engine();
void Reset();
const double* GetBest() const;
void SetRange(double minimum, double maximum);
void SetRange(unsigned int i, double minimum, double maximum);
void SetRange(const Vector<DIM>& minimum, const Vector<DIM>& maximum);
virtual double CalculateError(const double testcase[DIM], bool& stop) = 0;
void Reset();
bool Solve(unsigned int maxgenerations = 1000000000);
bool RunOneGeneration();
private:
void Select(unsigned int candidate, unsigned int *r1 = 0, unsigned int *r2 = 0, unsigned int *r3 = 0, unsigned int *r4 = 0, unsigned int *r5 = 0);
// randomly select unique individuals
void Select(
unsigned int candidate,
unsigned int *r1 = 0,
unsigned int *r2 = 0,
unsigned int *r3 = 0,
unsigned int *r4 = 0,
unsigned int *r5 = 0
);
// print a single individual to stdout
void Dump(const double& fitness, Vector<DIM>& individual) const;
void MakeTrial_best1exp(unsigned int candidate, Vector<DIM>& trial);
void MakeTrial_rand1exp(unsigned int candidate, Vector<DIM>& trial);
void MakeTrial_randtobest1exp(unsigned int candidate, Vector<DIM>& trial);
void MakeTrial_best2exp(unsigned int candidate, Vector<DIM>& trial);
void MakeTrial_rand2exp(unsigned int candidate, Vector<DIM>& trial);
void MakeTrial_best1bin(unsigned int candidate, Vector<DIM>& trial);
void MakeTrial_rand1bin(unsigned int candidate, Vector<DIM>& trial);
void MakeTrial_randtobest1bin(unsigned int candidate, Vector<DIM>& trial);
void MakeTrial_best2bin(unsigned int candidate, Vector<DIM>& trial);
void MakeTrial_rand2bin(unsigned int candidate, Vector<DIM>& trial);
private:
// DE algorithm
void MakeTrial_best1exp (unsigned int candidate, Vector<DIM>& trial);
void MakeTrial_rand1exp (unsigned int candidate, Vector<DIM>& trial);
void MakeTrial_randtobest1exp (unsigned int candidate, Vector<DIM>& trial);
void MakeTrial_best2exp (unsigned int candidate, Vector<DIM>& trial);
void MakeTrial_rand2exp (unsigned int candidate, Vector<DIM>& trial);
void MakeTrial_best1bin (unsigned int candidate, Vector<DIM>& trial);
void MakeTrial_rand1bin (unsigned int candidate, Vector<DIM>& trial);
void MakeTrial_randtobest1bin (unsigned int candidate, Vector<DIM>& trial);
void MakeTrial_best2bin (unsigned int candidate, Vector<DIM>& trial);
void MakeTrial_rand2bin (unsigned int candidate, Vector<DIM>& trial);
// constants, parameters
static const double DEFAULTSCALE;
static const double DEFAULTCROSSOVER;
static const double DEFAULTRANGE;
static const double BIGDOUBLE;
double scale;
double crossover;
// entropy source
Random prng;
private:
Random prng;
// population
Vector<DIM> best;
Vector<DIM> minimum;
Vector<DIM> maximum;
Vector<DIM> population[POP];
double fitness[POP];
double scale;
double crossover;
Vector<DIM> mean;
Vector<DIM> variance;
Vector<DIM> population[POPSIZE];
double fitness[POPSIZE];
double bestfitness;
};
// algorithm state
bool success;
unsigned int generation;
};
};