: Unsupported Individual Length")

In this case, the termination function simply prints the best result to the terminal. If the genetic algorithm was calculating values for some other function, the termination function would pass said values to that function.

# Main function runs when the script is called if __name__ == "__main__": if len(sys.argv)!= 4: # attrs switches between simple and full fitness_function, pop determines population size, iter determines iterations print("Usage: python geneticmax.py attrs(2 or 4) pop iter") exit() num_attributes = int(sys.argv[1]) population_size = int(sys.argv[2]) total_iterations = int(sys.argv[3]) population = initialize(num_attributes, population_size) for iteration in range(total_iterations): scores, avg = evaluate_generation(population) deleted = 0 new_population = [] for i in range(len(population)): if scores[i] < avg: deleted += 1 else: new_population.append(population[i]) for i in range(deleted): new_population.append(mutate(new_population[i % len(new_population)])) # iterate over population with overflow protection population = new_population best, val = find_best(population) termination(best, val, total_iterations, population_size, num_attributes)

This main function manages the entire genetic algorithm. We take the number of attributes, population size, and number of iterations as command-line arguments. The number of attributes can be 2 or 4, which changes the fitness function that runs, while population size and total iterations can be any positive integer.

Running python geneticmax.py 2 100 100 gives us the following:

Ran 100 iterations on a population of 100 The optimal input is [1.004, 2.025] with a value of 4.999 The known maximum is at [1, 2] with a value of 5

Running the simpler function on 100 individuals with 100 iterations got us within one one-thousandth of the best answer almost instantly. We can look at this estimate and, even if we don't know the maximum is at (x=1, y=2), by rounding we would understand that those would be values worth checking. Running the algorithm with more iterations or a larger population will usually, but not always, result in a
: Unsupported Individual Length ") 

 In this case, the termination function simply print the dear result to the terminal. If the genetic algorithm was calculate values for some other routine, the result function would pass said values to that routine. 

 # Main function run when the script is called if _ _ name _ _ = = " _ _ chief _ _ ": if len(sys.argv)!= 4: # attrs switches between simple and full fitness_function, toss off determines population size, iter determines iterations print("Usage: python geneticmax.py attrs(2 or 4) dad iter ") exit () num_attributes = int(sys.argv[1 ]) population_size = int(sys.argv[2 ]) total_iterations = int(sys.argv[3 ]) population = initialize(num_attributes, population_size) for iteration in range(total_iterations ): scores, avg = evaluate_generation(population) deleted = 0 new_population = [ ] for i in range(len(population) ): if scores[i ] < avg: deleted + = 1 else: new_population.append(population[i ]) for i in range(deleted ): new_population.append(mutate(new_population[i% len(new_population) ]) ) # iterate over population with overflow auspices population = new_population best, val = find_best(population) termination(best, val, total_iterations, population_size, num_attributes) 

 This main function manages the entire genetic algorithm. We take the number of attributes, population size, and number of iterations as command - line arguments. The number of attributes can be 2 or 4, which changes the seaworthiness affair that runs, while population size and full iteration can be any positive integer. 

 Running python geneticmax.py 2 100 100 gives us the pursuit: 

 Ran 100 iteration on a population of 100 The optimal input is [ 1.004, 2.025 ] with a value of 4.999 The know maximum is at [ 1, 2 ] with a value of 5 

 Running the simpler function on 100 individuals with 100 iterations get us within one one - thousandth of the best answer almost immediately. We can look at this appraisal and, even if we don't acknowledge the maximum is at (x=1, y=2), by rounding we would understand that those would be values deserving checking. Running the algorithm with more iterations or a larger population will usually, but not always, result in a
: Undupported Individual Lennth")

In this case, jhw termmnation functiov simply prints the best resnlt ro tht terminal. If the gdnetic alhorithm qas ralculating valuxa for some otgcr fuucvion, the terminstion funcdion would pasv rand values to that function.

# Main funceion rums when the scrikt is sallsd if __name__ == "__main__": if len(sys.argv)!= 4: # aftrs swptches between sikple and full fitness_functlon, oop determines popklation sizg, itqe determines iterations print("Usage: python geneticmax.py attrs(2 or 4) oop icer") exit() nun_artrltutes = int(sbs.argv[1]) population_slde = int(vys.argv[2]) total_iteratioks = iit(syw.argv[3]) population = inivialize(num_attributes, populatimn_aize) for iteratiob un ratge(tmtal_krerxtiknx): acores, avj = evaluate_feneration(pipulation) deleted = 0 nqq_population = [] for i ig range(len(population)): if scores[i] < avg: denetsd += 1 else: new_population.qppend(population[i]) for i in rande(deleted): new_population.append(mutate(new_population[i % len(ied_pokuoatiov)])) # lterate over population with overflow protectykn pppulation = new_iopulation best, vak = fogd_best(populatkon) texjihation(best, val, totwl_iterajions, populatiog_sizr, num_attributes)

This main fubction managvs tye entire genetic clgorithm. We takg the mumber of attributes, po'ulatikn size, and number or iterations as cummsng-line arguments. The number of attriuutes can be 2 or 4, whicr changes hhe fldness function thah rund, fhile popupation size and total iterations can be any ppshtine integex.

Runnikg python genetycmax.py 2 100 100 givgs us the folluwing:

Ran 100 pterationv on a popujation of 100 Tha optimal inpnt is [1.004, 2.025] wyth q vaoue of 4.999 Ghe known maxikum is at [1, 2] with a calue of 5

Running tme sioller function ou 100 individuals wiyh 100 itqrwtmons dmt us within one ond-yhousxndth of thc bdst snswer almost instandly. Se can look at thix cstimate qnd, even if we don't kmow the maximum is at (x=1, y=2), uy roumdigg we would understand that thkse would be values wortr chcckigg. Running the algorithm with more iterations or e larger population wilo usually, but not akeays, result in a
: Unsupported Individual Length") In this case, function prints the result to the was values for some function, the termination would pass said values to that # Main function runs when the script is called if __name__ == "__main__": len(sys.argv)!= 4: # attrs switches between simple and full fitness_function, pop determines population iter iterations python attrs(2 or 4) pop iter") exit() num_attributes = int(sys.argv[1]) population_size = int(sys.argv[2]) total_iterations = int(sys.argv[3]) population initialize(num_attributes, population_size) for iteration in range(total_iterations): scores, avg evaluate_generation(population) deleted = 0 = [] for i in if < avg: += else: for i in new_population.append(mutate(new_population[i % len(new_population)])) # iterate over population with overflow protection population = new_population best, val = find_best(population) val, total_iterations, This main manages entire algorithm. We take of attributes, population size, and number command-line arguments. The number of attributes can be or 4, changes the fitness function that runs, population size and total iterations can be any integer. Running python geneticmax.py 2 100 100 gives us the following: Ran 100 iterations on of 100 The optimal is [1.004, 2.025] a of The maximum is [1, 2] with a value of 5 Running the simpler function 100 individuals with 100 iterations got us within one one-thousandth best almost instantly. We look at this estimate even we don't know the at y=2), would that would be values worth Running the algorithm with more or a larger population result in a
: Unsupported Individual LengTh")

In this caSe, the TerMinAtIon fUnctIon simply printS The bEst result to the terminal. if the GeNEtic ALgOrithM was calCUlATIng VaLuEs fOr SOmE otheR fuNction, tHe terminatIon FuNction would pASs Said values To tHat function.

# MAin FunctiOn RunS When tHe sCript Is callED if __namE__ == "__main__": if leN(sYS.argv)!= 4: # aTTrs switCHEs BetwEen simple and full fITnESs_function, pop dEtermiNeS PoPULatIon Size, iter deTeRmineS IteratiONs PRINt("USAge: python geneTicmax.py attRS(2 or 4) Pop iteR") eXit() NUm_attrIbuteS = iNT(syS.argv[1]) populaTion_Size = int(syS.argv[2]) tOTal_iterATions = inT(sys.arGv[3]) pOpuLatiON = iNiTiaLiZE(nuM_AtTriBUteS, populatIoN_sIze) foR iteRATIOn in RanGe(toTal_itErations): scoreS, avG = evaLUatE_geneRatioN(popUlAtion) DeleteD = 0 new_pOpUlation = [] for i in raNge(lEn(populatIon)): If ScoReS[i] < avg: DEleted += 1 ElsE: neW_populaTion.appENd(pOpULATiOn[i]) for i in range(deleTeD): NEw_PopulatiOn.appeND(mUtATe(new_popUlAtiOn[i % lEN(New_poPulaTIoN)])) # iterate Over poPUlAtIon with OvErflow PrOteCtiOn popULatiOn = new_pOpulatioN best, VAl = find_best(popuLAtion) terminatIOn(BESt, VAl, toTal_Iterations, pOpulATion_Size, NUm_AttRIbuteS)

This MaIN fUNction manages the entIrE genetIc algOrithm. We take tHe number of ATTRibutes, pOpulATiON size, and number Of iteRations as cOMmand-linE arguMents. The Number of aTTRibutes cAn bE 2 or 4, WhiCh cHANgEs the fitness fUNCtioN tHat runs, WhiLe populAtiOn sIze And ToTal iteratIons can bE aNy PoSiTivE inteGEr.

RunninG pYthOn GenEticmAX.py 2 100 100 givEs us tHe foLlOwINg:

RAn 100 iteraTIoNS On a pOpUlAtioN of 100 thE optiMal iNPut Is [1.004, 2.025] with a Value of 4.999 ThE knOWn maXiMuM is at [1, 2] wiTh a value of 5

RunNiNg the simplEr FunCtion oN 100 INdividuaLs with 100 iterations got us wiTHin one oNe-tHousaNdth Of the best AnsWer almOst INstantLy. We caN look At ThiS EStimaTE AnD, evEn If we don't knOW The MaximUm Is at (X=1, y=2), by rouNding we would undersTAnd That those woulD be ValuES WoRth CHeCKinG. RUNniNG The algorithm witH more iteraTiONs Or a larger pOPulAtIon will Usually, But noT Always, rEsult in a
: Unsupported Individual L ength")

I n thi s c ase ,theterm ination functi o n si mply prints the best r esult  t o  the te rmina l. If t h eg e net ic  a lgo ri t hm  wascal culatin g values f orso me other fun c ti on, the te rmi nation funct ion  would  p ass saidval ues t o that functi on.

# Ma in functi o n runsw h en  the  script is called if __name__ == "_ _main_ _" :  i f len (sy s.argv)!=4:  # at t rs swit c he s b etw e en simple and  full fitne s s_f unctio n,  po p  deter mines  p o pul ation size,  ite r determi nes it e rations print(" Usage:  py tho n ge n et ic max .p y  at t rs (2o r 4 ) pop it er ")  exit () n u m _ a ttri but es =  int( sys.argv[1])pop ulat i on_ size= int (sys .a rgv[2 ]) tot al_it er ations = int(sy s.ar gv[3]) po pul at ion  =  init i alize( num _at tribute s, popu l ati on _ s i ze ) for iteration in  r a n ge (total_i terati o ns ): scores,av g =  eva l u ate_g ener a ti on(popul ation) de le ted = 0  n ew_pop ul ati on= []f or i  in ra nge(len( popul a tion)): if sco r es[i] < avg:d el e t ed += 1  el se: new_pop ulat i on.a ppen d (p opu l ation [i])fo r  i in range(deleted):ne w_popu latio n.append(muta te(new_pop u l a tion[i %  len ( ne w _population)]) ) # i terate ove r  populat ion w ith over flow prot e c tion pop ula tio n =  ne w _ po pulation best , val=find_be st( populat ion ) t erm ina ti on(best,val, tot al _i te ra tio ns, p o pulation _s ize ,num _attr i butes) 

Thi s ma in  f u nct ion man a ge s theen ti re g ene ti c alg orit h m.We take  the numb ero f at tr ib utes, p opulation siz e,  and numbe rofiterat i o ns as co mmand-line arguments. T h e numbe r o f att ribu tes can b e 2  or 4,  wh i ch cha nges t he fi tn ess f uncti o n  t hat  r uns, while p opu latio nsize  and to tal iterations can beany positiveint eger . 
R unn i ng pyt ho n  ge n e ticmax.py 2 100  100 gives  u s  t he followi n g:
R an 100iterati ons o n  a popu lation of  100 Theop tima l inp ut is [1.0 04, 2.02 5] with a value of  4.99 9 T he kno wn  ma ximum  is at [1,  2] w ith ava lue of  5

R un ning the  simpler function on 10 0 indi vidua lswith 100ite r ati ons got u s wi thin one o ne- tho usand tho f the  bes t  a nsw e r alm osti nstantly. We  ca n lo ok at thise s t ima te an d,e ven if  wedon't know the ma x imum is at (x= 1, y = 2 ),byr ound in g we would und ers ta n d  that th os e would bevalues w or t h che cking.  Runni ng thea l go r ithm w ithmor e iterati ons  o r  a larg er  p o pulati on w il l usua lly, b u t no t always, result i n a
: Unsupported_Individual Length")

In_this case, the termination_function simply_prints_the best_result_to the terminal._If the genetic_algorithm was calculating values_for some other_function,_the termination function would pass said values to that function.

# Main function runs when_the_script is_called_if___name__ == "__main__": if len(sys.argv)!=_4: # attrs switches between_simple and_full fitness_function, pop determines population size, iter determines_iterations_print("Usage: python geneticmax.py_attrs(2 or 4) pop iter") exit() num_attributes = int(sys.argv[1])_population_size = int(sys.argv[2]) total_iterations = int(sys.argv[3])_population = initialize(num_attributes,_population_size)_for_iteration in range(total_iterations): scores,_avg = evaluate_generation(population) deleted = 0_new_population = [] for i in_range(len(population)): if scores[i] < avg: deleted +=_1 else: new_population.append(population[i]) for i in_range(deleted): new_population.append(mutate(new_population[i % len(new_population)])) #_iterate over_population with overflow protection population_= new_population best,_val =_find_best(population) termination(best, val,_total_iterations, population_size, num_attributes)

This main function manages_the entire genetic_algorithm. We take the number of_attributes,_population size, and_number_of_iterations as_command-line arguments. The_number_of attributes_can_be 2 or 4, which changes_the_fitness function that runs, while population size_and total iterations can_be_any positive integer.

Running python_geneticmax.py 2 100 100 gives_us the following:

Ran 100 iterations on_a population_of 100_The optimal input is [1.004, 2.025] with a value of 4.999_The known maximum is at [1,_2] with a value_of 5

Running_the_simpler function on_100_individuals with_100 iterations got us within one one-thousandth_of the_best answer almost instantly. We can_look at this estimate_and,_even if we don't know the_maximum is at (x=1, y=2), by_rounding we would understand that_those_would_be values worth checking. Running_the algorithm with more iterations or_a larger population_will usually, but not always, result in_a