How to resolve the algorithm Evolutionary algorithm step by step in the Fortran programming language
How to resolve the algorithm Evolutionary algorithm step by step in the Fortran programming language
Table of Contents
Problem Statement
Starting with:
Note: to aid comparison, try and ensure the variables and functions mentioned in the task description appear in solutions
A cursory examination of a few of the solutions reveals that the instructions have not been followed rigorously in some solutions. Specifically, Note that some of the the solutions given retain characters in the mutated string that are correct in the target string. However, the instruction above does not state to retain any of the characters while performing the mutation. Although some may believe to do so is implied from the use of "converges" Strictly speaking, the new parent should be selected from the new pool of mutations, and then the new parent used to generate the next set of mutations with parent characters getting retained only by not being mutated. It then becomes possible that the new set of mutations has no member that is fitter than the parent! As illustration of this error, the code for 8th has the following remark. NOTE: this has been changed, the 8th version is completely random now Clearly, this algo will be applying the mutation function only to the parent characters that don't match to the target characters! To ensure that the new parent is never less fit than the prior parent, both the parent and all of the latest mutations are subjected to the fitness test to select the next parent.
Let's start with the solution:
Step by Step solution about How to resolve the algorithm Evolutionary algorithm step by step in the Fortran programming language
Source code in the fortran programming language
!***************************************************************************************************
module evolve_routines
!***************************************************************************************************
implicit none
!the target string:
character(len=*),parameter :: targ = 'METHINKS IT IS LIKE A WEASEL'
contains
!***************************************************************************************************
!********************************************************************
pure elemental function fitness(member) result(n)
!********************************************************************
! The fitness function. The lower the value, the better the match.
! It is zero if they are identical.
!********************************************************************
implicit none
integer :: n
character(len=*),intent(in) :: member
integer :: i
n=0
do i=1,len(targ)
n = n + abs( ichar(targ(i:i)) - ichar(member(i:i)) )
end do
!********************************************************************
end function fitness
!********************************************************************
!********************************************************************
pure elemental subroutine mutate(member,factor)
!********************************************************************
! mutate a member of the population.
!********************************************************************
implicit none
character(len=*),intent(inout) :: member !population member
real,intent(in) :: factor !mutation factor
integer,parameter :: n_chars = 27 !number of characters in set
character(len=n_chars),parameter :: chars = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ '
real :: rnd_val
integer :: i,j,n
n = len(member)
do i=1,n
rnd_val = rand()
if (rnd_val<=factor) then !mutate this element
rnd_val = rand()
j = int(rnd_val*n_chars)+1 !an integer between 1 and n_chars
member(i:i) = chars(j:j)
end if
end do
!********************************************************************
end subroutine mutate
!********************************************************************
!***************************************************************************************************
end module evolve_routines
!***************************************************************************************************
!***************************************************************************************************
program evolve
!***************************************************************************************************
! The main program
!***************************************************************************************************
use evolve_routines
implicit none
!Tuning parameters:
integer,parameter :: seed = 12345 !random number generator seed
integer,parameter :: max_iter = 10000 !maximum number of iterations
integer,parameter :: population_size = 200 !size of the population
real,parameter :: factor = 0.04 ![0,1] mutation factor
integer,parameter :: iprint = 5 !print every iprint iterations
!local variables:
integer :: i,iter
integer,dimension(1) :: i_best
character(len=len(targ)),dimension(population_size) :: population
!initialize random number generator:
call srand(seed)
!create initial population:
! [the first element of the population will hold the best member]
population(1) = 'PACQXJB CQPWEYKSVDCIOUPKUOJY' !initial guess
iter=0
write(*,'(A10,A30,A10)') 'iter','best','fitness'
write(*,'(I10,A30,I10)') iter,population(1),fitness(population(1))
do
iter = iter + 1 !iteration counter
!write the iteration:
if (mod(iter,iprint)==0) write(*,'(I10,A30,I10)') iter,population(1),fitness(population(1))
!check exit conditions:
if ( iter>max_iter .or. fitness(population(1))==0 ) exit
!copy best member and mutate:
population = population(1)
do i=2,population_size
call mutate(population(i),factor)
end do
!select the new best population member:
! [the best has the lowest value]
i_best = minloc(fitness(population))
population(1) = population(i_best(1))
end do
!write the last iteration:
if (mod(iter,iprint)/=0) write(*,'(I10,A30,I10)') iter,population(1),fitness(population(1))
if (iter>max_iter) then
write(*,*) 'No solution found.'
else
write(*,*) 'Solution found.'
end if
!***************************************************************************************************
end program evolve
!***************************************************************************************************
iter best fitness
0 PACQXJB CQPWEYKSVDCIOUPKUOJY 459
5 PACDXJBRCQP EYKSVDK OAPKGOJY 278
10 PAPDJJBOCQP EYCDKDK A PHGQJF 177
15 PAUDJJBO FP FY VKBL A PEGQJF 100
20 PEUDJMOO KP FY IKLD A YECQJF 57
25 PEUHJMOT KU FS IKLD A YECQJL 35
30 PEUHJMIT KU GS LKJD A YEAQFL 23
35 MERHJMIT KT IS LHJD A YEASFL 15
40 MERHJMKS IT IS LIJD A WEASFL 7
45 MERHINKS IT IS LIJD A WEASFL 5
50 MERHINKS IT IS LIJD A WEASEL 4
55 MERHINKS IT IS LIKD A WEASEL 3
60 MESHINKS IT IS LIKD A WEASEL 2
65 MESHINKS IT IS LIKD A WEASEL 2
70 MESHINKS IT IS LIKE A WEASEL 1
75 METHINKS IT IS LIKE A WEASEL 0
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