aimacode · articuno12 · Mar 11, 2017 · Mar 11, 2017 · Mar 11, 2017 · Mar 11, 2017
diff --git a/search.py b/search.py
@@ -570,30 +570,37 @@ def LRTA_cost(self, s, a, s1, H):
 # Genetic Algorithm
 
 
-def genetic_search(problem, fitness_fn, ngen=1000, pmut=0.1, n=20):
+def genetic_search(problem, fitness_fn,gene_bound,ngen=1000, optimal_value=10000000,pmut=0.1, n=20,initial_population=None):
     """
     Call genetic_algorithm on the appropriate parts of a problem.
     This requires the problem to have states that can mate and mutate,
-    plus a value method that scores states."""
-    s = problem.initial_state
-    states = [problem.result(s, a) for a in problem.actions(s)]
-    random.shuffle(states)
-    return genetic_algorithm(states[:n], problem.value, ngen, pmut)
-
-
-def genetic_algorithm(population, fitness_fn, ngen=1000, pmut=0.1):
+    plus a value method that scores states.These states are passed as initial
+    population to the search"""
+    if(initial_population == None) :
+        raise Exception("Initial population not given in genetic search")
+    else :
+        random.shuffle(initial_population)
+        newfitness_fn = lambda inidividual : fitness_fn(inidividual.genes)
+        population = [GAState(initial_population[i]) for i in range(len(initial_population))]
+        best_individual = genetic_algorithm(population[:n],newfitness_fn,gene_bound,optimal_value, ngen, pmut)
+        return best_individual.genes
+
+def genetic_algorithm(population, fitness_fn, gene_bound,optimal_value=10000000, ngen=1000, pmut=0.1):
     "[Figure 4.8]"
-    for i in range(ngen):
+    for i in range(int(ngen)):
         new_population = []
-        for i in range(len(population)):
+        for j in range(len(population)):
             fitnesses = map(fitness_fn, population)
             p1, p2 = weighted_sample_with_replacement(population, fitnesses, 2)
             child = p1.mate(p2)
             if random.uniform(0, 1) < pmut:
-                child.mutate()
+                child.mutate(gene_bound)
             new_population.append(child)
         population = new_population
-    return argmax(population, key=fitness_fn)
+        current_bestindividual = argmax(population, key=fitness_fn)
+        if(fitness_fn(current_bestindividual) >= optimal_value) :
+            return current_bestindividual
+    return current_bestindividual
 
 
 class GAState:
@@ -608,9 +615,10 @@ def mate(self, other):
         c = random.randrange(len(self.genes))
         return self.__class__(self.genes[:c] + other.genes[c:])
 
-    def mutate(self):
-        "Change a few of my genes."
-        raise NotImplementedError
+    def mutate(self,gene_bound) :
+        "Change one of my genes."
+        index = random.choice(range(len(self.genes)))
+        self.genes[index] = random.choice(range(gene_bound[0],gene_bound[1]))
 
 # _____________________________________________________________________________
 # The remainder of this file implements examples for the search algorithms.
@@ -884,6 +892,16 @@ def goal_test(self, state):
         return not any(self.conflicted(state, state[col], col)
                        for col in range(len(state)))
 
+    def value(self,state):
+        """Returns value corresponding to a state where value is defined as
+        the number of pairs of non-attacking queens"""
+        attacking_sum = 0
+        for c1 in range(len(state)):
+            if not state[c1] == None :
+                for c2 in range(c1+1,len(state)):
+                    if not state[c2] == None :
+                        attacking_sum += self.conflict(state[c1],c1,state[c2],c2)
+        return (self.N*(self.N - 1))/2 - attacking_sum
 # ______________________________________________________________________________
 # Inverse Boggle: Search for a high-scoring Boggle board. A good domain for
 # iterative-repair and related search techniques, as suggested by Justin Boyan.

diff --git a/tests/test_search.py b/tests/test_search.py
@@ -86,6 +86,18 @@ def test_LRTAStarAgent():
     my_agent = LRTAStarAgent(LRTA_problem)
     assert my_agent('State_5') is None
 
+def test_genetic_search():
+    N = 5
+    nqueens_problem = NQueensProblem(N)
+    initial_population = []
+    gene_bound = (0,N)
+    for i in range(N * 20) :
+        population = [random.choice(range(N)) for gene in range(N)]
+        initial_population.append(population)
+    result = genetic_search(nqueens_problem,nqueens_problem.value,gene_bound,(N*(N-1))/2,1000,0.1,N * 20,initial_population)
+    for col1 in range(len(result)) :
+        for col2 in range(col1+1,len(result)) :
+            assert nqueens_problem.conflict(result[col1],col1,result[col2],col2) == False
 
 # TODO: for .ipynb:
 """