Implemented 8queens and n-color problems fitness function

problem.py

@@ -0,0 +1,99 @@
+from mimetypes import init
+from search import *
+from timeit import default_timer as timer
+from datetime import timedelta
+import time
+def switch(type_of_problem):
+    if type_of_problem=="queen":
+        erfolgreiche_laufe=0
+        succeded_gen = 0
+        aes = 0
+        start = time.time()
+        for i in range(1):
+            x,a = problem(100,8,fitness_fn_queens,[1,2,3,4,5,6,7,8],500,28,0.1)
+            print(x,fitness_fn_queens(x))
+            if(fitness_fn_queens(x)==28):
+                succeded_gen += a
+                erfolgreiche_laufe += 1
+        if erfolgreiche_laufe!=0:
+            aes = succeded_gen/erfolgreiche_laufe
+        sr = erfolgreiche_laufe/100
+        end = time.time()
+        ausfuhrungszeit = end - start
+        ##init_pop = 5
+        ##pmut = 0.125
+        ##ngen = 1000
+
+        print(
+        "(AES)Average Evaluations to a Solution(Geschwindigkeit) ist {}".format(aes)+
+        "\n(SR)Success Rate(Lösungswahrscheinlichkeit) ist {}".format(sr)+
+        "\nAusführungszeit ist {}".format(ausfuhrungszeit))
+    else:
+        erfolgreiche_laufe=0
+        succeded_gen = 0
+        aes = 0
+        start = time.time()
+        for i in range(100):
+            x,a = problem(3,6,fitness_fn_landkart,["red","blue","grey"],200,6,0.01)
+            print(x,fitness_fn_landkart(x))
+            if(fitness_fn_landkart(x)==6):
+                succeded_gen += a
+                erfolgreiche_laufe += 1
+        if erfolgreiche_laufe!=0:
+            aes = succeded_gen/erfolgreiche_laufe
+        sr = erfolgreiche_laufe/100
+        end = time.time()
+        ausfuhrungszeit = end - start
+        init_pop = 5
+        pmut = 0.125
+        ngen = 1000
+
+        print("Einstellungen -> init_pop = {}, gen = {}, mut = {}\n".format(init_pop,ngen,pmut)+
+        "(AES)Average Evaluations to a Solution(Geschwindigkeit) ist {}".format(aes)+
+        "\n(SR)Success Rate(Lösungswahrscheinlichkeit) ist {}".format(sr)+
+        "\nAusführungszeit ist {}".format(ausfuhrungszeit))    
+def main():
+    switch("queen")
+
+
+def problem(init_p,state_length,fitness_fn,gene_pool,ngen,f_thres,pmut):
+    population = init_population(init_p,gene_pool,state_length)
+    x,a = genetic_algorithm(population = population,fitness_fn=fitness_fn,gene_pool=gene_pool,ngen=ngen,f_thres= f_thres,pmut= pmut)
+    return x,a
+
+def fitness_fn_queens(individuum):
+    fitness_ct = 28
+    for idx, x in enumerate(individuum):
+        for idx2,x2 in enumerate(individuum):
+            if(idx>=idx2):
+                continue
+            else:
+                if(abs(idx-idx2)==abs(x-x2)): # 2,4    5,1   
+                    fitness_ct-=1
+                if(x==x2):
+                    fitness_ct-=1
+
+    return fitness_ct
+
+def fitness_fn_landkart(individuum):
+    fitness_ct = 6
+    if(individuum[1]==individuum[2]):
+        fitness_ct -= 1
+    if(individuum[0]==individuum[1] or individuum[0]==individuum[2]):
+        if(individuum[0]==individuum[1]==individuum[2]):
+            fitness_ct -= 2
+        else:
+            fitness_ct -= 1
+    if(individuum[1]==individuum[3] or individuum[2]==individuum[3]):
+        if(individuum[1]==individuum[2]==individuum[3]):
+            fitness_ct -= 2
+        else:
+            fitness_ct -= 1
+    if(individuum[3]==individuum[4]):
+        fitness_ct-=1
+    return fitness_ct
+
+if __name__ == "__main__":
+    main()
+
+

search.py

@@ -930,9 +930,9 @@ def genetic_algorithm(population, fitness_fn, gene_pool=[0, 1], f_thres=None, ng
 
         fittest_individual = fitness_threshold(fitness_fn, f_thres, population)
         if fittest_individual:
-            return fittest_individual
+            return fittest_individual,i
 
-    return max(population, key=fitness_fn)
+    return max(population, key=fitness_fn),-1
 
 
 def fitness_threshold(fitness_fn, f_thres, population):