pC.new_random_genes()
pC.eval_all()
print 'DNAs of pC:'
print pC.get_DNAs()
pC.print_stuff(slim=True)
for dude in pC:
dude.plot_FMsynth_solution()
# and the modified version
pD=Population(FMsynthC,ps,dummyfunc,searchspace)
pD.set_ncase(2)
for dude in pD:
dude.w[1]=4.7
dude.initialize_target()
pD.copy_otherpop(pC)
pD.eval_all()
print 'DNAs of pD:'
print pD.get_DNAs()
pD.print_stuff(slim=True)
for dude in pD:
dude.plot_FMsynth_solution()
"""
An interesting next step would be inplementing the option of more than 6
problem dimensions ... probably one might also be able to convince oneself that
the problem complexity grows very nonlinearily with problem dimension
"""
ps = 4 # population size dim = len(searchspace) # search space dimension # now let's create a population to test version A of the objective function implementation pA = Population(Individual, ps, FMsynthA, searchspace) pA.new_random_genes() pA.eval_all() print 'DNAs of pA:' print pA.get_DNAs() pA.print_stuff(slim=True) # and here the objective function version B and another population to test it problem_instance = FMsynthB() objfuncB = problem_instance.call pB = Population(Individual, ps, objfuncB, searchspace) pB.copy_otherpop(pA) pB.eval_all() print 'DNAs of pB:' print pB.get_DNAs() pB.print_stuff(slim=True) # -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- #--- plot routine: solution candidate visualisation # -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- """ The really good thing about this FM synthesis test problem is that it is a difficult six-dimensional test problem, but it is easy to plot a solution candidate and judge at the first glance whether a solution is good or not. I think, how essential the combination of these two properties is, cannot be stressed enough.
ps=4 # population size dim=len(searchspace) # search space dimension # now let's create a population to test version A of the objective function implementation pA=Population(Individual,ps,FMsynthA,searchspace) pA.new_random_genes() pA.eval_all() print 'DNAs of pA:' print pA.get_DNAs() pA.print_stuff(slim=True) # and here the objective function version B and another population to test it problem_instance=FMsynthB() objfuncB=problem_instance.call pB=Population(Individual,ps,objfuncB,searchspace) pB.copy_otherpop(pA) pB.eval_all() print 'DNAs of pB:' print pB.get_DNAs() pB.print_stuff(slim=True) # -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- #--- plot routine: solution candidate visualisation # -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- """ The really good thing about this FM synthesis test problem is that it is a difficult six-dimensional test problem, but it is easy to plot a solution candidate and judge at the first glance whether a solution is good or not. I think, how essential the combination of these two properties is, cannot be stressed enough.
