# Print current best score
print scores[0][0]
return scores[0][1]
# There are nine outbound and inbound flights respectively for every person
# so the domain in the list is set to (0,8) repeated twice for each person
domain = [(0, 8)] * (len(gto.people) * 2) # [(0,8), (0,8), ..., ]
# Optimizing
s = geneticoptimize(domain, gto.schedulecost)
print s
# Pull the data and present
gto.schedulecost(s)
gto.printschedule(s)
# In case the code yield error: This code is working
# Just try several times and it will run
# Expected answer
# Total cost: 3533
# sol = [4, 3, 3, 3, 4, 3, 3, 0, 0, 0, 0, 0]
# Seymour BOS 12:34-15:02 $109 10:33-12:03 $ 74
# Franny DAL 10:30-14:57 $290 10:51-14:16 $256
# Zooey CAK 10:53-13:36 $189 10:32-13:16 $139
# Walt MIA 11:28-14:40 $248 12:37-15:05 $170
# Buddy ORD 12:44-14:17 $134 10:33-13:11 $132
# Les OMA 11:08-13:07 $175 11:07-13:24 $171
# If best != current, the new sol will be used to loop again
return sol
# There are nine outbound and inbound flights respectively for every person
# so the domain in the list is set to (min,max) repeated for each person
domain = [(0,8)]*(len(gto.people)*2) # [(0,8), (0,8), ..., ]
# Optimizing
s = hillclimb(domain, gto.schedulecost)
print s
# Pull the data and present
gto.schedulecost(s)
gto.printschedule(s)
# Example case
# sol = [1, 4, 3, 2, 7, 3, 6, 3, 2, 4, 5, 3]
# neighbours =
# [[2, 4, 3, 2, 7, 3, 6, 3, 2, 4, 5, 3],
# [0, 4, 3, 2, 7, 3, 6, 3, 2, 4, 5, 3],
# [1, 5, 3, 2, 7, 3, 6, 3, 2, 4, 5, 3],
# [1, 3, 3, 2, 7, 3, 6, 3, 2, 4, 5, 3],
# [1, 4, 4, 2, 7, 3, 6, 3, 2, 4, 5, 3],
# [1, 4, 2, 2, 7, 3, 6, 3, 2, 4, 5, 3],
# [1, 4, 3, 3, 7, 3, 6, 3, 2, 4, 5, 3],
# [1, 4, 3, 1, 7, 3, 6, 3, 2, 4, 5, 3],
# [1, 4, 3, 2, 8, 3, 6, 3, 2, 4, 5, 3],
