# pikaia_module.f90 Source File

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## Source Code

!*****************************************************************************************
!>
!  PIKAIA is a general purpose unconstrained optimization
!  method based on a genetic algorithm.
!  This is an object-oriented version of the algorithm for Fortran 2003/2008.
!
!  * [Original description page](http://www.hao.ucar.edu/modeling/pikaia/pikaia.php)
!
!
!    Copyright (c) 2015, Jacob Williams
!
!    http://github.com/jacobwilliams/pikaia
!
!
!    Redistribution and use in source and binary forms, with or without
!    modification, are permitted provided that the following conditions are met:
!    * Redistributions of source code must retain the above copyright notice, this
!      list of conditions and the following disclaimer.
!    * 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.
!    * Neither the name of pikaia nor the names of its
!      contributors may be used to endorse or promote products derived from
!      this software without specific prior written permission.
!
!    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "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 THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
!    FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
!    DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
!    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
!    CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
!    OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
!    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
!
!    ------------------------------------------------------------------------------
!
!    The original version of the PIKAIA software is public domain software
!    and is available electronically from the High Altitude Observatory.
!    http://www.hao.ucar.edu/modeling/pikaia/pikaia.php
!
!
!# History
!  * Jacob Williams : 3/8/2015 : Significant refactoring of original PIKAIA code.
!    Converted to free-form source, double precision real variables, added various
!    new features, and an object-oriented interface.
!
!*****************************************************************************************

module pikaia_module

use,intrinsic :: iso_fortran_env
!$use omp_lib implicit none private integer,parameter :: wp = real64 !! Default real kind [8 bytes]. !********************************************************* type,public :: pikaia_class !! Main class for using the Pikaia algorithm. !! INIT and SOLVE are the only public methods. private integer :: n = 0 !number of solution variables real(wp),dimension(:),allocatable :: xl !! lower bounds of x real(wp),dimension(:),allocatable :: xu !! upper bound of x real(wp),dimension(:),allocatable :: del !other solution inputs (with default values): integer :: np = 100 integer :: ngen = 500 integer :: nd = 5 real(wp) :: pcross = 0.85_wp integer :: imut = 2 real(wp) :: pmuti = 0.005_wp !! initial value of pmut real(wp) :: pmutmn = 0.0005_wp real(wp) :: pmutmx = 0.25_wp real(wp) :: fdif = 1.0_wp integer :: irep = 1 integer :: ielite = 1 integer :: ivrb = 0 real(wp) :: convergence_tol = 0.0001_wp integer :: convergence_window = 20 integer :: iseed = 999 real(wp) :: initial_guess_frac = 0.1_wp !used internally: real(wp) :: pmut = -huge(1.0_wp) real(wp) :: bestft = huge(1.0_wp) real(wp) :: pmutpv = huge(1.0_wp) !user-supplied procedures: procedure(pikaia_func),pointer :: user_f => null() !! fitness function procedure(iter_func),pointer :: iter_f => null() !! reporting function (best member of population) contains !public routines: procedure,non_overridable,public :: init => set_inputs procedure,non_overridable,public :: solve => solve_with_pikaia !private routines: procedure,non_overridable :: ff => func_wrapper !! internal pikaia function (x:[0,1]) procedure,non_overridable :: newpop procedure,non_overridable :: stdrep procedure,non_overridable :: genrep procedure,non_overridable :: adjmut procedure,non_overridable :: cross procedure,non_overridable :: encode procedure,non_overridable :: mutate procedure,non_overridable :: decode procedure,non_overridable :: select_parents procedure,non_overridable :: report procedure,non_overridable :: rnkpop procedure,non_overridable :: pikaia end type pikaia_class !********************************************************* abstract interface subroutine pikaia_func(me,x,f) !! The interface for the function that pikaia will be maximizing. import :: wp,pikaia_class implicit none class(pikaia_class),intent(inout) :: me !! pikaia class real(wp),dimension(:),intent(in) :: x !! optimization variable vector real(wp),intent(out) :: f !! fitness value end subroutine pikaia_func subroutine iter_func(me,iter,x,f) !! The interface for the function that user can specify !! to report each iteration when pikaia is running. !! The best (fittest) population member is passed to !! this routine in each generation. import :: wp,pikaia_class implicit none class(pikaia_class),intent(inout) :: me !! pikaia class integer,intent(in) :: iter !! iteration number real(wp),dimension(:),intent(in) :: x !! optimization variable vector real(wp),intent(in) :: f !! fitness value end subroutine iter_func end interface contains !***************************************************************************************** !***************************************************************************************** !> author: Jacob Williams ! ! Constructor for the [[pikaia_class]]. ! The routine must be called before the solve routine can be used. ! ! The following inputs are required: n, f, xl, xu. ! For the others, if they are not present, then ! the default values are used ! !@note Based on setctl in the original code. subroutine set_inputs(me,& n,xl,xu,f,status,& iter_f,& np,ngen,nd,pcross,pmutmn,pmutmx,pmut,imut,& fdif,irep,ielite,ivrb,& convergence_tol,convergence_window,initial_guess_frac,& iseed) implicit none class(pikaia_class),intent(out) :: me !! pikaia class integer,intent(in) :: n !! the parameter space dimension, i.e., the number !! of adjustable parameters (size of the x vector). real(wp),dimension(n),intent(in) :: xl !! vector of lower bounds for x real(wp),dimension(n),intent(in) :: xu !! vector of upper bounds for x procedure(pikaia_func) :: f !! user-supplied scalar function of n variables, !! which must have the [[pikaia_func]] procedure interface. !! By convention, f should return higher values for more optimal !! parameter values (i.e., individuals which are more "fit"). !! For example, in fitting a function through data points, f !! could return the inverse of chi**2. integer,intent(out) :: status !! status output flag (0 if there were no errors) procedure(iter_func),optional :: iter_f !! user-supplied subroutine that will report the !! best solution for each generation. !! It must have the [[iter_func]] procedure interface. If not present, !! then it is not used. (note: this is independent of ivrb). integer,intent(in),optional :: np !! number of individuals in a population (default is 100) integer,intent(in),optional :: ngen !! maximum number of iterations integer,intent(in),optional :: nd !! number of significant digits (i.e., number of !! genes) retained in chromosomal encoding (default is 6). real(wp),intent(in),optional :: pcross !! crossover probability; must be <= 1.0 (default !! is 0.85). If crossover takes place, either one !! or two splicing points are used, with equal !! probabilities real(wp),intent(in),optional :: pmutmn !! minimum mutation rate; must be >= 0.0 (default is 0.0005) real(wp),intent(in),optional :: pmutmx !! maximum mutation rate; must be <= 1.0 (default is 0.25) real(wp),intent(in),optional :: pmut !! initial mutation rate; should be small (default !! is 0.005) (Note: the mutation rate is the probability !! that any one gene locus will mutate in !! any one generation.) integer,intent(in),optional :: imut !! mutation mode; 1/2/3/4/5 (default is 2). !! 1=one-point mutation, fixed rate. !! 2=one-point, adjustable rate based on fitness. !! 3=one-point, adjustable rate based on distance. !! 4=one-point+creep, fixed rate. !! 5=one-point+creep, adjustable rate based on fitness. !! 6=one-point+creep, adjustable rate based on distance. real(wp),intent(in),optional :: fdif !! relative fitness differential; range from 0 !! (none) to 1 (maximum). (default is 1.0) integer,intent(in),optional :: irep !! reproduction plan; 1/2/3=Full generational !! replacement/Steady-state-replace-random/Steady- !! state-replace-worst (default is 3) integer,intent(in),optional :: ielite !! elitism flag; 0/1=off/on (default is 0) !! (Applies only to reproduction plans 1 and 2) integer,intent(in),optional :: ivrb !! printed output 0/1/2=None/Minimal/Verbose !! (default is 0) real(wp),intent(in),optional :: convergence_tol !! convergence tolerance; must be > 0.0 (default is 0.0001) integer,intent(in),optional :: convergence_window !! convergence window; must be >= 0 !! This is the number of consecutive solutions !! within the tolerance for convergence to !! be declared (default is 20) real(wp),intent(in),optional :: initial_guess_frac !! fraction of the initial population !! to set equal to the initial guess. Range from 0 !! (none) to 1.0 (all). (default is 0.1 or 10%). integer,intent(in),optional :: iseed !! random seed value; must be > 0 (default is 999) me%n = n if (allocated(me%xl)) deallocate(me%xl) allocate(me%xl(n)) me%xl = xl if (allocated(me%xu)) deallocate(me%xu) allocate(me%xu(n)) me%xu = xu if (allocated(me%del)) deallocate(me%del) allocate(me%del(n)) me%del = me%xu - me%xl me%user_f => f if (present(iter_f)) me%iter_f => iter_f if (present(np )) me%np = np if (present(ngen )) me%ngen = ngen if (present(nd )) me%nd = nd if (present(pcross )) me%pcross = pcross if (present(imut )) me%imut = imut if (present(pmut )) me%pmuti = pmut !initial value if (present(pmutmn )) me%pmutmn = pmutmn if (present(pmutmx )) me%pmutmx = pmutmx if (present(fdif )) me%fdif = fdif if (present(irep )) me%irep = irep if (present(ielite )) me%ielite = ielite if (present(ivrb )) me%ivrb = ivrb if (present(convergence_tol )) me%convergence_tol = convergence_tol if (present(convergence_window )) me%convergence_window = convergence_window if (present(initial_guess_frac )) me%initial_guess_frac = initial_guess_frac if (present(iseed )) me%iseed = iseed !check for errors: !initialize error flag: status = 0 !Print a header if (me%ivrb>0) then write(output_unit,'(A)') '------------------------------------------------------------' write(output_unit,'(A)') ' PIKAIA Genetic Algorithm Report ' write(output_unit,'(A)') '------------------------------------------------------------' write(output_unit,'(A,I4)') ' Number of Generations evolving: ',me%ngen write(output_unit,'(A,I4)') ' Individuals per generation: ',me%np write(output_unit,'(A,I4)') ' Number of Chromosome segments: ',me%n write(output_unit,'(A,I4)') ' Length of Chromosome segments: ',me%nd write(output_unit,'(A,E11.4)') ' Crossover probability: ',me%pcross write(output_unit,'(A,E11.4)') ' Initial mutation rate: ',me%pmuti write(output_unit,'(A,E11.4)') ' Minimum mutation rate: ',me%pmutmn write(output_unit,'(A,E11.4)') ' Maximum mutation rate: ',me%pmutmx write(output_unit,'(A,E11.4)') ' Relative fitness differential: ',me%fdif write(output_unit,'(A,E11.4)') ' Initial guess fraction: ',me%initial_guess_frac write(output_unit,'(A,E11.4)') ' Convergence tolerance: ',me%convergence_tol write(output_unit,'(A,I4)') ' Convergence window: ',me%convergence_window select case (me%imut) case(1); write(output_unit,'(A)') ' Mutation Mode: Uniform, Constant Rate' case(2); write(output_unit,'(A)') ' Mutation Mode: Uniform, Variable Rate (F)' case(3); write(output_unit,'(A)') ' Mutation Mode: Uniform, Variable Rate (D)' case(4); write(output_unit,'(A)') ' Mutation Mode: Uniform+Creep, Constant Rate' case(5); write(output_unit,'(A)') ' Mutation Mode: Uniform+Creep, Variable Rate (F)' case(6); write(output_unit,'(A)') ' Mutation Mode: Uniform+Creep, Variable Rate (D)' end select select case (me%irep) case(1); write(output_unit,'(A)') ' Reproduction Plan: Full generational replacement' case(2); write(output_unit,'(A)') ' Reproduction Plan: Steady-state-replace-random' case(3); write(output_unit,'(A)') ' Reproduction Plan: Steady-state-replace-worst' end select write(output_unit,'(A)') '------------------------------------------------------------' end if !Check some control values if (me%imut/=1 .and. me%imut/=2 .and. me%imut/=3 .and. & me%imut/=4 .and. me%imut/=5 .and. me%imut/=6) then write(output_unit,'(A)') ' ERROR: illegal value for Mutation Mode.' status = 5 end if if (me%fdif>1.0_wp) then write(output_unit,'(A)') ' ERROR: illegal value for Relative fitness differential.' status = 9 end if if (me%irep/=1 .and. me%irep/=2 .and. me%irep/=3) then write(output_unit,'(A)') ' ERROR: illegal value for Reproduction plan.' status = 10 end if if (me%pcross>1.0_wp .or. me%pcross<0.0_wp) then write(output_unit,'(A)') ' ERROR: illegal value for Crossover probability.' status = 4 end if if (me%ielite/=0 .and. me%ielite/=1) then write(output_unit,'(A)') ' ERROR: illegal value for Elitism flag.' status = 11 end if if (me%convergence_tol<=0.0_wp) then write(output_unit,'(A)') ' ERROR: illegal value for Convergence tolerance.' status = 101 end if if (me%convergence_window<=0) then write(output_unit,'(A)') ' ERROR: illegal value for Convergence window.' status = 102 end if if (me%iseed<=0) then write(output_unit,'(A)') ' ERROR: illegal value for iseed.' status = 103 end if if (me%nd>9 .or. me%nd<1) then write(output_unit,'(A)') ' ERROR: illegal value for Chromosome length.' status = 104 end if if (mod(me%np,2)>0) then write(output_unit,'(A)') ' ERROR: population size must be an even number.' status = 105 end if if (me%initial_guess_frac<0.0_wp .or. me%initial_guess_frac>1.0_wp) then write(output_unit,'(A)') ' ERROR: illegal value for Initial guess fraction.' status = 106 end if if (me%irep==1 .and. me%imut==1 .and. me%pmuti>0.5_wp .and. me%ielite==0) then write(output_unit,'(A)') & ' WARNING: dangerously high value for Initial mutation rate; '//& '(Should enforce elitism with ielite=1.)' end if if (me%irep==1 .and. me%imut==2 .and. me%pmutmx>0.5_wp .and. me%ielite==0) then write(output_unit,'(A)') & ' WARNING: dangerously high value for Maximum mutation rate; '//& '(Should enforce elitism with ielite=1.)' end if if (me%fdif<0.33_wp .and. me%irep/=3) then write(output_unit,'(A)') & ' WARNING: dangerously low value of Relative fitness differential.' end if end subroutine set_inputs !***************************************************************************************** !***************************************************************************************** !> ! Optimization (maximization) of user-supplied "fitness" function ! over n-dimensional parameter space x using a basic genetic ! algorithm method. ! ! Genetic algorithms are heuristic search techniques that ! incorporate in a computational setting, the biological notion ! of evolution by means of natural selection. This subroutine ! implements the three basic operations of selection, crossover, ! and mutation, operating on "genotypes" encoded as strings. ! ! Version 1.2 differs from version 1.0 (December 1995) in that ! it includes (1) two-point crossover, (2) creep mutation, and ! (3) dynamical adjustment of the mutation rate based on metric ! distance in parameter space. ! !# Authors ! * Paul Charbonneau & Barry Knapp ! (High Altitude Observatory, National Center for Atmospheric Research) ! Version 1.2 [ 2002 April 3 ] ! * Jacob Williams : 3/8/3015 : Refactoring and some new features. ! !# References ! * Charbonneau, Paul. "An introduction to genetic algorithms for ! numerical optimization", NCAR Technical Note TN-450+IA ! (April 2002) ! * Charbonneau, Paul. "Release Notes for PIKAIA 1.2", ! NCAR Technical Note TN-451+STR (April 2002) ! * Charbonneau, Paul, and Knapp, Barry. "A User's Guide ! to PIKAIA 1.0" NCAR Technical Note TN-418+IA ! (December 1995) ! * Goldberg, David E. Genetic Algorithms in Search, Optimization, ! & Machine Learning. Addison-Wesley, 1989. ! * Davis, Lawrence, ed. Handbook of Genetic Algorithms. ! Van Nostrand Reinhold, 1991. subroutine pikaia(me,x,f,status) implicit none !subroutine arguments: class(pikaia_class),intent(inout) :: me real(wp),dimension(:),intent(inout) :: x !! Input - initial guess for solution vector. !! Output - the "fittest" (optimal) solution found, !! i.e., the solution which maximizes the fitness function. real(wp),intent(out) :: f !! the (scalar) value of the fitness function at x integer,intent(out) :: status !! an indicator of the success or failure !! of the call to pikaia (0=success; non-zero=failure) !Local variables integer :: k,ip,ig,ip1,ip2,new,newtot,istart,i_window real(wp) :: current_best_f, last_best_f, fguess logical :: convergence real(wp),dimension(me%n,2) :: ph real(wp),dimension(me%n,me%np) :: oldph real(wp),dimension(me%n,me%np) :: newph integer,dimension(me%n*me%nd) :: gn1 integer,dimension(me%n*me%nd) :: gn2 integer,dimension(me%np) :: ifit integer,dimension(me%np) :: jfit real(wp),dimension(me%np) :: fitns real(wp),dimension(me%n) :: xguess real(wp),parameter :: big = huge(1.0_wp) !! a large number !initialize: call rninit(me%iseed) me%bestft = -big me%pmutpv = -big me%pmut = me%pmuti !set initial mutation rate (it can change) i_window = 0 last_best_f = -big convergence = .false. status = 0 !Handle the initial guess: if (me%initial_guess_frac==0.0_wp) then !initial guess not used (totally random population) istart = 1 !index to start random population members else !use the initial guess: xguess = x do k=1,me%n !make sure they are all within the [0,1] bounds xguess(k) = max( 0.0_wp, min(1.0_wp,xguess(k)) ) end do call me%ff(xguess,fguess) !how many elements in the population to set to xguess?: ! [at least 1, at most n] istart = max(1, min(me%np, int(me%np * me%initial_guess_frac))) do k=1,istart oldph(:,k) = xguess fitns(k) = fguess end do istart = istart + 1 !index to start random population members end if !Compute initial (random but bounded) phenotypes do ip=istart,me%np do k=1,me%n oldph(k,ip)=urand() !from [0,1] end do end do !$omp parallel do private(ip)
do ip=istart,me%np
call me%ff(oldph(:,ip),fitns(ip))
end do
!$omp end parallel do !Rank initial population by fitness order call me%rnkpop(fitns,ifit,jfit) !Main Generation Loop do ig=1,me%ngen !Main Population Loop newtot=0 do ip=1,me%np/2 !1. pick two parents call me%select_parents(jfit,ip1,ip2) !2. encode parent phenotypes call me%encode(oldph(:,ip1),gn1) call me%encode(oldph(:,ip2),gn2) !3. breed call me%cross(gn1,gn2) call me%mutate(gn1) call me%mutate(gn2) !4. decode offspring genotypes call me%decode(gn1,ph(:,1)) call me%decode(gn2,ph(:,2)) !5. insert into population if (me%irep==1) then call me%genrep(ip,ph,newph) else call me%stdrep(ph,oldph,fitns,ifit,jfit,new) newtot = newtot+new end if end do !End of Main Population Loop !if running full generational replacement: swap populations if (me%irep==1) call me%newpop(oldph,newph,ifit,jfit,fitns,newtot) !adjust mutation rate? if (any(me%imut==[2,3,5,6])) call adjmut(me,oldph,fitns,ifit) !report this iteration: if (me%ivrb>0) call me%report(oldph,fitns,ifit,ig,newtot) !report (unscaled) x: if (associated(me%iter_f)) & call me%iter_f(ig,me%xl+me%del*oldph(1:me%n,ifit(me%np)),fitns(ifit(me%np))) !JW additions: add a convergence criteria ! [stop if the last convergence_window iterations are all within the convergence_tol] current_best_f = fitns(ifit(me%np)) !current iteration best fitness if (abs(current_best_f-last_best_f)<=me%convergence_tol) then !this solution is within the tol from the previous one i_window = i_window + 1 !number of solutions within the convergence tolerance else i_window = 0 !a significantly better solution was found, reset window end if if (i_window>=me%convergence_window) then convergence = .true. exit !exit main loop -> convergence end if last_best_f = current_best_f !to compare with next iteration end do !End of Main Generation Loop !JW additions: if (me%ivrb>0) then if (convergence) then write(output_unit,'(A)') 'Solution Converged' else write(output_unit,'(A)') 'Iteration Limit Reached' end if end if !Return best phenotype and its fitness x = oldph(1:me%n,ifit(me%np)) f = fitns(ifit(me%np)) end subroutine pikaia !***************************************************************************************** !***************************************************************************************** !> author: Jacob Williams ! ! Main pikaia wrapper used by the class. subroutine solve_with_pikaia(me,x,f,status) implicit none class(pikaia_class),intent(inout) :: me real(wp),dimension(:),intent(inout) :: x real(wp),intent(out) :: f integer,intent(out) :: status if (associated(me%user_f)) then !scale input initial guess to be [0,1]: x = (x-me%xl)/me%del !call the main routine, using the wrapper function: call me%pikaia(x,f,status) !unscale output to be [xl,xu]: x = me%xl + me%del*x else write(output_unit,'(A)') 'Error: pikaia class not initialized.' status = -1 end if end subroutine solve_with_pikaia !***************************************************************************************** !***************************************************************************************** !> author: Jacob Williams ! ! Wrapper for the user's function that is used by the main pikaia routine ! The x input to this function comes from pikaia, and will be between [0,1]. subroutine func_wrapper(me,x,f) implicit none class(pikaia_class),intent(inout) :: me ! pikaia class real(wp),dimension(:),intent(in) :: x ! optimization variable vector [0,1] real(wp),intent(out) :: f ! fitness value real(wp),dimension(me%n) :: xp !unscaled x vector: [xu,xl] !map each x variable from [0,1] to [xl,xu]: xp = me%xl + me%del*x !call the user's function with xp: call me%user_f(xp,f) end subroutine func_wrapper !***************************************************************************************** !***************************************************************************************** !> author: B. G. Knapp ! date: 86/12/23 ! ! Return integer array p which indexes array a in increasing order. ! Array a is not disturbed. The Quicksort algorithm is used. ! !# Reference ! * N. Wirth, "Algorithms and Data Structures", Prentice-Hall, 1986 subroutine rqsort(n,a,p) implicit none integer,intent(in) :: n real(wp),dimension(n),intent(in) :: a integer,dimension(n),intent(out) :: p !Constants integer,parameter :: LGN = 32 !! log base 2 of maximum n integer,parameter :: Q = 11 !! smallest subfile to use quicksort on !Local: integer,dimension(LGN) :: stackl,stackr real(wp) :: x integer :: s,t,l,m,r,i,j !Initialize the stack stackl(1)=1 stackr(1)=n s=1 !Initialize the pointer array p = [(i, i=1,n)] do while (s>0) l=stackl(s) r=stackr(s) s=s-1 3 if ((r-l)<Q) then !Use straight insertion do i=l+1,r t = p(i) x = a(t) do j=i-1,l,-1 if (a(p(j))<=x) goto 5 p(j+1) = p(j) end do j=l-1 5 p(j+1) = t end do else !Use quicksort, with pivot as median of a(l), a(m), a(r) m=(l+r)/2 t=p(m) if (a(t)<a(p(l))) then p(m)=p(l) p(l)=t t=p(m) end if if (a(t)>a(p(r))) then p(m)=p(r) p(r)=t t=p(m) if (a(t)<a(p(l))) then p(m)=p(l) p(l)=t t=p(m) end if end if !Partition x=a(t) i=l+1 j=r-1 do while (i<=j) do while (a(p(i))<x) i=i+1 end do do while (x<a(p(j))) j=j-1 end do if (i<=j) then t=p(i) p(i)=p(j) p(j)=t i=i+1 j=j-1 end if end do !Stack the larger subfile s=s+1 if ((j-l)>(r-i)) then stackl(s)=l stackr(s)=j l=i else stackl(s)=i stackr(s)=r r=j end if goto 3 end if end do end subroutine rqsort !***************************************************************************************** !***************************************************************************************** !> author: Jacob Williams ! date: 3/8/2015 ! ! Return the next pseudo-random deviate from a sequence which is ! uniformly distributed in the interval [0,1] ! !@note This is now just a wrapper for the intrinsic random_number function. function urand() result(r) implicit none real(wp) :: r call random_number(r) end function urand !***************************************************************************************** !***************************************************************************************** !> author: Jacob Williams ! date: 3/8/2015 ! ! Initialize the random number generator with the input seed value. ! !@note This is now just a wrapper for the intrinsic random_seed function. subroutine rninit(iseed) implicit none integer,intent(in) :: iseed integer,dimension(:),allocatable :: seed integer :: n call random_seed(size=n) allocate(seed(n)) seed = iseed call random_seed(put=seed) deallocate(seed) end subroutine rninit !***************************************************************************************** !***************************************************************************************** !> ! Write generation report to standard output subroutine report(me,oldph,fitns,ifit,ig,nnew) implicit none class(pikaia_class),intent(inout) :: me real(wp),dimension(me%n,me%np),intent(in) :: oldph real(wp),dimension(me%np),intent(in) :: fitns integer,dimension(me%np),intent(in) :: ifit integer,intent(in) :: ig integer,intent(in) :: nnew integer :: ndpwr,k logical :: rpt rpt=.false. if (me%pmut/=me%pmutpv) then me%pmutpv=me%pmut rpt=.true. end if if (fitns(ifit(me%np))/=me%bestft) then me%bestft=fitns(ifit(me%np)) rpt=.true. end if if (rpt .or. me%ivrb>=2) then !Power of 10 to make integer genotypes for display ndpwr = 10**me%nd write(output_unit,'(/I6,I6,F10.6,4F10.6)') & ig,nnew,me%pmut,fitns(ifit(me%np)),& fitns(ifit(me%np-1)),fitns(ifit(me%np/2)) do k=1,me%n write(output_unit,'(22X,3I10)') & nint(ndpwr*oldph(k,ifit(me%np))),& nint(ndpwr*oldph(k,ifit(me%np-1))),& nint(ndpwr*oldph(k,ifit(me%np/2))) end do end if end subroutine report !***************************************************************************************** !***************************************************************************************** !> ! Encode phenotype parameters into integer genotype ! ph(k) are x,y coordinates [ 0 < x,y < 1 ] subroutine encode(me,ph,gn) implicit none class(pikaia_class),intent(inout) :: me real(wp),dimension(me%n),intent(in) :: ph integer,dimension(me%n*me%nd),intent(out) :: gn integer :: ip,i,j,ii real(wp) :: z z=10.0_wp**me%nd ii=0 do i=1,me%n ip=int(ph(i)*z) do j=me%nd,1,-1 gn(ii+j)=mod(ip,10) ip=ip/10 end do ii=ii+me%nd end do end subroutine encode !***************************************************************************************** !***************************************************************************************** !> ! decode genotype into phenotype parameters ! ph(k) are x,y coordinates [ 0 < x,y < 1 ] subroutine decode(me,gn,ph) implicit none class(pikaia_class),intent(inout) :: me integer,dimension(me%n*me%nd),intent(in) :: gn real(wp),dimension(me%n),intent(out) :: ph integer :: ip,i,j,ii real(wp) :: z z=10.0_wp**(-me%nd) ii=0 do i=1,me%n ip=0 do j=1,me%nd ip=10*ip+gn(ii+j) end do ph(i)=ip*z ii=ii+me%nd end do end subroutine decode !***************************************************************************************** !***************************************************************************************** !> ! breeds two parent chromosomes into two offspring chromosomes. ! breeding occurs through crossover. If the crossover probability ! test yields true (crossover taking place), either one-point or ! two-point crossover is used, with equal probabilities. ! !@note Compatibility with version 1.0: To enforce 100% use of one-point ! crossover, un-comment appropriate line in source code below subroutine cross(me,gn1,gn2) implicit none class(pikaia_class),intent(inout) :: me integer,dimension(me%n*me%nd),intent(inout) :: gn1 integer,dimension(me%n*me%nd),intent(inout) :: gn2 integer :: i, ispl, ispl2, itmp, t !Use crossover probability to decide whether a crossover occurs if (urand()<me%pcross) then !Compute first crossover point ispl=int(urand()*me%n*me%nd)+1 !Now choose between one-point and two-point crossover if (urand()<0.5_wp) then ispl2=me%n*me%nd else ispl2=int(urand()*me%n*me%nd)+1 !Un-comment following line to enforce one-point crossover !ispl2=me%n*me%nd if (ispl2<ispl) then itmp=ispl2 ispl2=ispl ispl=itmp end if end if !Swap genes from ispl to ispl2 do i=ispl,ispl2 t=gn2(i) gn2(i)=gn1(i) gn1(i)=t end do end if end subroutine cross !***************************************************************************************** !***************************************************************************************** !> ! Introduces random mutation in a genotype. ! Mutations occur at rate pmut at all gene loci. ! !# Input ! * imut=1 Uniform mutation, constant rate ! * imut=2 Uniform mutation, variable rate based on fitness ! * imut=3 Uniform mutation, variable rate based on distance ! * imut=4 Uniform or creep mutation, constant rate ! * imut=5 Uniform or creep mutation, variable rate based on fitness ! * imut=6 Uniform or creep mutation, variable rate based on distance subroutine mutate(me,gn) implicit none class(pikaia_class),intent(inout) :: me integer,dimension(me%n*me%nd),intent(inout) :: gn integer :: i,j,k,l,ist,inc,loc logical :: fix !Decide which type of mutation is to occur if (me%imut>=4 .and. urand()<=0.5_wp) then !CREEP MUTATION OPERATOR !Subject each locus to random +/- 1 increment at the rate pmut do i=1,me%n do j=1,me%nd if (urand()<me%pmut) then !Construct integer loc=(i-1)*me%nd+j inc=nint( urand() )*2-1 ist=(i-1)*me%nd+1 gn(loc)=gn(loc)+inc !This is where we carry over the one (up to two digits) !first take care of decrement below 0 case if (inc<0 .and. gn(loc)<0) then if (j==1) then gn(loc)=0 else fix = .true. do k=loc,ist+1,-1 gn(k)=9 gn(k-1)=gn(k-1)-1 if ( gn(k-1)>=0 ) then fix = .false. exit end if end do if (fix) then !we popped under 0.00000 lower bound; fix it up if ( gn(ist)<0) then do l=ist,loc gn(l)=0 end do end if end if end if end if if (inc>0 .and. gn(loc)>9) then if (j==1) then gn(loc)=9 else fix = .true. do k=loc,ist+1,-1 gn(k)=0 gn(k-1)=gn(k-1)+1 if ( gn(k-1)<=9 ) then fix = .false. exit end if end do if (fix) then !we popped over 9.99999 upper bound; fix it up if ( gn(ist)>9 ) then do l=ist,loc gn(l)=9 end do end if end if end if end if end if end do end do else !UNIFORM MUTATION OPERATOR !Subject each locus to random mutation at the rate pmut do i=1,me%n*me%nd if (urand()<me%pmut) then gn(i)=int(urand()*10.0_wp) end if end do end if end subroutine mutate !***************************************************************************************** !***************************************************************************************** !> ! Dynamical adjustment of mutation rate: ! ! * imut=2 or imut=5 : adjustment based on fitness differential ! between best and median individuals ! * imut=3 or imut=6 : adjustment based on metric distance ! between best and median individuals subroutine adjmut(me,oldph,fitns,ifit) implicit none class(pikaia_class),intent(inout) :: me integer,dimension(me%np),intent(in) :: ifit real(wp),dimension(me%n,me%np),intent(in) :: oldph real(wp),dimension(me%np),intent(in) :: fitns integer :: i real(wp) :: rdif real(wp),parameter :: rdiflo = 0.05_wp real(wp),parameter :: rdifhi = 0.25_wp real(wp),parameter :: delta = 1.5_wp if (me%imut==2 .or. me%imut==5) then !Adjustment based on fitness differential rdif = abs(fitns(ifit(me%np)) - & fitns(ifit(me%np/2)))/(fitns(ifit(me%np)) + & fitns(ifit(me%np/2))) else if (me%imut==3 .or. me%imut==6) then !Adjustment based on normalized metric distance rdif=0.0_wp do i=1,me%n rdif=rdif+( oldph(i,ifit(me%np))-oldph(i,ifit(me%np/2)) )**2 end do rdif=sqrt( rdif ) / real(me%n,wp) end if if (rdif<=rdiflo) then me%pmut=min(me%pmutmx,me%pmut*delta) else if (rdif>=rdifhi) then me%pmut=max(me%pmutmn,me%pmut/delta) end if end subroutine adjmut !***************************************************************************************** !***************************************************************************************** !> ! Selects two parents from the population, using roulette wheel ! algorithm with the relative fitnesses of the phenotypes as ! the "hit" probabilities. ! !# Reference ! * Davis 1991, chap. 1. ! !# History ! * Jacob Williams : 3/10/2015 : rewrote this routine to return both parents, ! and also protect against the loop exiting without selecting a parent. subroutine select_parents(me,jfit,imom,idad) implicit none class(pikaia_class),intent(inout) :: me integer,dimension(me%np),intent(in) :: jfit integer,intent(out) :: imom integer,intent(out) :: idad integer :: np1,i,j real(wp) :: dice,rtfit integer,dimension(2) :: parents !initialize: np1 = me%np+1 parents = -99 !get two (unequal) parents: do j=1,2 main: do dice = urand()*me%np*np1 rtfit = 0.0_wp do i=1,me%np rtfit = rtfit+np1+me%fdif*(np1-2*jfit(i)) if (rtfit>=dice) then parents(j) = i if (parents(1)/=parents(2)) exit main end if end do end do main end do imom = parents(1) idad = parents(2) end subroutine select_parents !***************************************************************************************** !***************************************************************************************** !> ! Ranks initial population. ! Calls external sort routine to produce key index and rank order ! of input array arrin (which is not altered). subroutine rnkpop(me,arrin,indx,rank) implicit none class(pikaia_class),intent(inout) :: me real(wp),dimension(me%np),intent(in) :: arrin integer,dimension(me%np),intent(out) :: indx integer,dimension(me%np),intent(out) :: rank integer :: i !Compute the key index call rqsort(me%np,arrin,indx) !and the rank order do i=1,me%np rank(indx(i)) = me%np-i+1 end do end subroutine rnkpop !***************************************************************************************** !***************************************************************************************** !> ! Full generational replacement: accumulate offspring into new ! population array subroutine genrep(me,ip,ph,newph) implicit none class(pikaia_class),intent(inout) :: me integer,intent(in) :: ip real(wp),dimension(me%n,2),intent(in) :: ph real(wp),dimension(me%n,me%np),intent(out) :: newph integer :: i1,i2,k !Insert one offspring pair into new population i1=2*ip-1 i2=i1+1 do k=1,me%n newph(k,i1)=ph(k,1) newph(k,i2)=ph(k,2) end do end subroutine genrep !***************************************************************************************** !***************************************************************************************** !> ! Steady-state reproduction: insert offspring pair into population ! only if they are fit enough (replace-random if irep=2 or ! replace-worst if irep=3). subroutine stdrep(me,ph,oldph,fitns,ifit,jfit,nnew) implicit none class(pikaia_class),intent(inout) :: me real(wp),dimension(me%n,2),intent(in) :: ph real(wp),dimension(me%n,me%np),intent(inout) :: oldph real(wp),dimension(me%np),intent(inout) :: fitns integer,dimension(me%np),intent(inout) :: ifit integer,dimension(me%np),intent(inout) :: jfit integer,intent(out) :: nnew integer :: i,j,k,i1,if1 real(wp) :: fit nnew = 0 main_loop : do j=1,2 !1. compute offspring fitness (with caller's fitness function) call me%ff(ph(:,j),fit) !2. if fit enough, insert in population do i=me%np,1,-1 if (fit>fitns(ifit(i))) then !make sure the phenotype is not already in the population if (i<me%np) then if (all(oldph(1:me%n,ifit(i+1))==ph(1:me%n,j))) cycle main_loop end if !offspring is fit enough for insertion, and is unique !(i) insert phenotype at appropriate place in population if (me%irep==3) then i1=1 else if (me%ielite==0 .or. i==me%np) then i1=int(urand()*me%np)+1 else i1=int(urand()*(me%np-1))+1 end if if1 = ifit(i1) fitns(if1)=fit do k=1,me%n oldph(k,if1)=ph(k,j) end do !(ii) shift and update ranking arrays if (i<i1) then !shift up jfit(if1)=me%np-i do k=i1-1,i+1,-1 jfit(ifit(k))=jfit(ifit(k))-1 ifit(k+1)=ifit(k) end do ifit(i+1)=if1 else !shift down jfit(if1)=me%np-i+1 do k=i1+1,i jfit(ifit(k))=jfit(ifit(k))+1 ifit(k-1)=ifit(k) end do ifit(i)=if1 end if nnew = nnew+1 cycle main_loop end if end do end do main_loop end subroutine stdrep !***************************************************************************************** !***************************************************************************************** !> ! Replaces old population by new; recomputes fitnesses & ranks ! !# History ! * Jacob Williams : 3/9/2015 : avoid unnecessary function evaluation if ielite/=1. subroutine newpop(me,oldph,newph,ifit,jfit,fitns,nnew) implicit none class(pikaia_class),intent(inout) :: me real(wp),dimension(me%n,me%np),intent(inout) :: oldph real(wp),dimension(me%n,me%np),intent(inout) :: newph integer,dimension(me%np),intent(out) :: ifit integer,dimension(me%np),intent(out) :: jfit real(wp),dimension(me%np),intent(out) :: fitns integer,intent(out) :: nnew integer :: i real(wp) :: f nnew = me%np if (me%ielite==1) then !if using elitism, introduce in new population fittest of old !population (if greater than fitness of the individual it is !to replace) call me%ff(newph(:,1),f) if (f<fitns(ifit(me%np))) then newph(:,1)=oldph(:,ifit(me%np)) nnew = nnew-1 end if end if !replace population !$omp parallel do private(i)
do i=1,me%np

oldph(:,i)=newph(:,i)

!get fitness using caller's fitness function
call me%ff(oldph(:,i),fitns(i))

end do
!\$omp end parallel do

!compute new population fitness rank order
call me%rnkpop(fitns,ifit,jfit)

end subroutine newpop
!*****************************************************************************************

!*****************************************************************************************
end module pikaia_module
!*****************************************************************************************