ga_population
Creates an Imsls_f_population data structure from user supplied individuals.
Synopsis
#include <imsls.h>
Imsls_f_population *imsls_f_ga_population, (int n, Imsls_f_chromosome *chromosome, Imsls_f_individual *individual[], ..., 0)
The type double function is imsls_d_ga_population.
Required Arguments
int n (Input)
The number of individuals in the population.
Imsls_f_chromosome *chromosome (Input)
A chromosome data structure created by imsls_f_ga_chromosome describing the chromosome encoding for individuals.
Imsls_f_individual *individual[] (Input)
An array of pointers to n individuals.
Return Value
Function
imsls_f_ga_population returns an
Imsls_f_population data structure, which is required input to
imsls_f_genetic_algorithm. The memory allocated to this data structure can be released using
imsls_f_ga_free_population.
Synopsis with Optional Arguments
#include <imsls.h>
Imsls_f_population *imsls_f_ga_population (int n, Imsls_f_chromosome *chromosome, Imsls_f_individual *individual[],
IMSLS_PRINT,
IMSLS_GRAY_ENCODING,
IMSLS_FITNESS, float fitness[],
IMSLS_FITNESS_FCN, float fitness(),
IMSLS_FITNESS_FCN_WITH_PARMS, float fitness(), void *parms,
0)
Optional Arguments
IMSLS_PRINT, (Input)
By default, intermediate results are not printed. This option turns on printing of intermediate results.
IMSLS_GRAY_ENCODING, (Input)
Specifies whether alleles are encoded using Base-2 or Gray encoding for integer and real phenotypes.
Default: Base-2 encoding.
IMSLS_FITNESS, float fitness[] (Input)
An array of length n containing the fitness values for the individuals in the population. fitness[i] is the fitness for the i-th individual.
IMSLS_FITNESS_FCN, float fitness (Imsls_f_individual *individual) (Input)
User-supplied function to calculate fitness for individual. If this is supplied, fitness values are calculated for each individual and included within the expanded population data structure. Otherwise they are set to zero.
IMSLS_FITNESS_FCN_WITH_PARMS, float fitness (Imsls_f_individual *individual, void *parms), void *parms (Input)
User-supplied function to calculate fitness for individual. If this is supplied, fitness values are calculated for each individual and included in the expanded population data structure. The parameters in parms are passed to the function.
Description
The
imsls_f_genetic_algorithm operates on a population of individuals.
ga_population allows users to systematically create an initial population by adding individuals to that population. It takes the individuals created using
imsls_f_ga_individual and encapsulates them into an
Imsls_f_population data structure.
Example
This example creates a population of 40 individuals each with one binary, two nominal, three integer and two real phenotypes. The
IMSLS_PRINT argument is used to print a description of the population. A simple fitness function calculation is used to illustrate how fitness values can be used to initialize a population with the
IMSLS_FITNESS argument. If fitness is not initialized, the fitness array in the data structure is set to
NULL. It can be initialized using an optional argument with
imsls_f_genetic_algorithm.
#include <imsls.h>
#include <stdio.h>
#include <stdlib.h>
int main(){
int i;
/* number of phenotypes by category */
int n_binary=1, n_nominal=2, n_integer=3, n_real=2;
int n = 40; /* population size */
int irandom[1]; /* temporary working storage */
float rrandom[1]; /* temporary working storage */
int binaryPhenotype[] = {1}; /* binary phenotype */
/* number of categories for nomial phenotypes */
int n_categories[] = {2, 3};
/* nominal phenotype values */
int nominalPhenotype[] = {1, 2};
/* number of intervals and boundaries for integer */
/* phenotypes */
int i_intervals[] = {16, 16, 16};
int i_bounds[] = {0, 1000, -10, 10, -20, 0};
/* integer phenotype values */
int integerPhenotype[] = {200, -5, -5};
/* number of intervals and boundaries for real */
/* phenotypes */
int r_intervals[] = {512, 1024};
float r_bounds[] = {0.0, 20.0, -20.0, 20.0};
/* real phenotype values */
float realPhenotype[] = {19.9, 19.9};
/* fitness array for individuals */
float fitness[40];
/* Chromosome Data Structure */
Imsls_f_chromosome* chromosome=NULL;
/* Individual Data Structure */
Imsls_f_individual* individuals[40];
/* Population Data Structure */
Imsls_f_population* population=NULL;
chromosome = imsls_f_ga_chromosome(
IMSLS_BINARY, n_binary,
IMSLS_NOMINAL, n_nominal, n_categories,
IMSLS_INTEGER, n_integer, i_intervals, i_bounds,
IMSLS_REAL, n_real, r_intervals, r_bounds, 0);
imsls_random_seed_set(12345);
/* Create individuals */
printf("Creating %d Individuals\n", n);
for(i=0; i<n; i++){
/* generate random values for phenotypes */
imsls_f_random_binomial(1, 1, 0.5,
IMSLS_RETURN_USER, binaryPhenotype, 0);
imsls_f_random_uniform_discrete(1, n_categories[0],
IMSLS_RETURN_USER, irandom, 0);
nominalPhenotype[0] = irandom[0]-1;
imsls_f_random_uniform_discrete(1, n_categories[1],
IMSLS_RETURN_USER, irandom, 0);
nominalPhenotype[1] = irandom[0]-1;
imsls_f_random_uniform_discrete(1, i_bounds[1]-i_bounds[0],
IMSLS_RETURN_USER, irandom, 0);
integerPhenotype[0] = irandom[0]-1;
imsls_f_random_uniform_discrete(1, i_bounds[3]-i_bounds[2],
IMSLS_RETURN_USER, irandom, 0);
integerPhenotype[1] = irandom[0]-1+i_bounds[2];
imsls_f_random_uniform_discrete(1, i_bounds[5]-i_bounds[4],
IMSLS_RETURN_USER, irandom, 0);
integerPhenotype[2] = irandom[0]-1+i_bounds[4];
imsls_f_random_uniform(1, IMSLS_RETURN_USER, rrandom, 0);
realPhenotype[0] =
rrandom[0] * (r_bounds[1]-r_bounds[0]) + r_bounds[0];
imsls_f_random_uniform(1, IMSLS_RETURN_USER, rrandom, 0);
realPhenotype[1] =
rrandom[0]*(r_bounds[3]-r_bounds[2]) + r_bounds[2];
/* create individual from these phenotypes */
individuals[i] = imsls_f_ga_individual(chromosome,
IMSLS_BINARY, binaryPhenotype,
IMSLS_NOMINAL, nominalPhenotype,
IMSLS_INTEGER, integerPhenotype,
IMSLS_REAL, realPhenotype, 0);
/* calculate fitness for this individual */
fitness[i] = 100.0 + 10*binaryPhenotype[0];
fitness[i] += 2*nominalPhenotype[1] - 4*nominalPhenotype[0];
fitness[i] += 0.0001*integerPhenotype[0] +
abs(integerPhenotype[1]+integerPhenotype[2])*0.1;
fitness[i] += 0.1*realPhenotype[0];
if (realPhenotype[1]>0) fitness[i] += 0.2*realPhenotype[1];
else fitness[i] -= 0.2*realPhenotype[1];
}
printf("Creating Population from %d Individuals\n", n);
population = imsls_f_ga_population(n, chromosome, individuals,
IMSLS_FITNESS, fitness, IMSLS_PRINT, 0);
imsls_free(chromosome);
for(i=0; i<n; i++) imsls_f_ga_free_individual(individuals[i]);
imsls_f_ga_free_population(population);
}
Output
The IMSLS_PRINT option produced the following description of the population. A summary of the population chromosome structure and fitness are printed followed by detailed information for the first 5 individuals in the population.
This example also illustrates the bit ordering within chromosomes. Nominal phenotypes are placed in the first bits followed by binary and encoded integers and real phenotypes.
Creating 40 Individuals
Creating Population from 40 Individuals
Population Size: 40
Average Fitness: 109.472527
Std. Dev. Fitness: 5.927261
Maximum Fitness: 120.244392
Minimum Fitness: 98.221916
Chromosome:
*******************************
**** CHROMOSOME STRUCTURE *****
Chromosome length: 34 Bits
*****BIT ASSIGNMENTS***********
Binary: 2 - 2 n_binary = 1
Nominal: 1 - 2 n_nominal= 2
Integer: 3 - 14 n_integer= 3
Real: 15 - 33 n_real = 2
*******************************
NOMINAL CATEGORIES*************
Variable 0: 2 categories
Variable 1: 3 categories
*******************************
INTEGER BOUNDS*****************
Variable 0: [ 0, 1000]
Variable 1: [ -10, 10]
Variable 2: [ -20, 0]
*******************************
INTEGER BITS*******************
Variable 0: 4 bits
Variable 1: 4 bits
Variable 2: 4 bits
*******************************
INTEGER DISCRETE INTERVALS*****
Variable 0: 16 intervals
Variable 1: 16 intervals
Variable 2: 16 intervals
*******************************
REAL BOUNDS********************
Variable 0: [0,20]
Variable 1: [-20,20]
*******************************
REAL BITS**********************
Variable 0: 9 bits
Variable 1: 10 bits
*******************************
REAL DISCRETE INTERVALS********
Variable 0: 512 intervals
Variable 1: 1024 intervals
*******************************
First 5 Individuals
****** INDIVIDUAL 0 ************************************************
Number of Parents: 2
Encoding: BASE-2
Fitness: 105.114510
PHENOTYPES
*************BINARY************
Variable 0: 0
************NOMINAL************
Variable 0: 1
Variable 1: 2
************INTEGER************
Variable 0: 35
Variable 1: -10
Variable 2: -19
**************REAL*************
Variable 0: 15.3157
Variable 1: 3.39719
**********CHROMOSOME************************************************
1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 1 0 0 1 0 1 0 1 1 0
********************************************************************
****** INDIVIDUAL 1 ************************************************
Number of Parents: 2
Encoding: BASE-2
Fitness: 111.796173
PHENOTYPES
*************BINARY************
Variable 0: 1
************NOMINAL************
Variable 0: 1
Variable 1: 0
************INTEGER************
Variable 0: 195
Variable 1: -5
Variable 2: -5
**************REAL*************
Variable 0: 19.6777
Variable 1: -14.0445
**********CHROMOSOME************************************************
1 0 1 0 0 1 1 0 1 0 0 1 1 0 0 1 1 1 1 1 0 1 1 1 0 0 1 0 0 1 1 0 0 0
********************************************************************
****** INDIVIDUAL 2 ************************************************
Number of Parents: 2
Encoding: BASE-2
Fitness: 104.841797
PHENOTYPES
*************BINARY************
Variable 0: 0
************NOMINAL************
Variable 0: 0
Variable 1: 0
************INTEGER************
Variable 0: 167
Variable 1: 7
Variable 2: -16
**************REAL*************
Variable 0: 18.3331
Variable 1: -10.4589
**********CHROMOSOME************************************************
0 0 0 0 0 1 0 1 1 0 1 0 0 1 1 1 1 1 0 1 0 1 0 1 0 0 1 1 1 1 0 1 0 0
********************************************************************
****** INDIVIDUAL 3 ************************************************
Number of Parents: 2
Encoding: BASE-2
Fitness: 110.905807
PHENOTYPES
*************BINARY************
Variable 0: 1
************NOMINAL************
Variable 0: 1
Variable 1: 0
************INTEGER************
Variable 0: 629
Variable 1: 0
Variable 2: -17
**************REAL*************
Variable 0: 18.213
Variable 1: -6.608
**********CHROMOSOME************************************************
1 0 1 1 0 1 0 1 0 0 0 0 0 1 0 1 1 1 0 1 0 0 1 0 0 1 0 1 0 1 0 1 1 0
********************************************************************
****** INDIVIDUAL 4 ************************************************
Number of Parents: 2
Encoding: BASE-2
Fitness: 114.371025
PHENOTYPES
*************BINARY************
Variable 0: 1
************NOMINAL************
Variable 0: 1
Variable 1: 2
************INTEGER************
Variable 0: 51
Variable 1: 8
Variable 2: -3
**************REAL*************
Variable 0: 7.13049
Variable 1: -15.7644
**********CHROMOSOME************************************************
1 2 1 0 0 0 0 1 1 1 0 1 1 0 1 0 1 0 1 1 0 1 1 0 0 0 0 1 1 0 1 1 0 0
********************************************************************
