y = [ sin(1*x0) * sin(2*x1) ] + [ sin(3*x2) * sin(4*x3) ] + ... + [ sin(49*x48) * sin(50*x49) ]
請求解 y 之最大值?
本問題我使用 pyevolve 函式庫來幫我處理染色體的突變、重組、交配工作。我只需要提供目標函數(eval_func)的計算方式即可。本問題我的 y 最佳解是 20.4676 ，決策變數是 [33, 26, 36, 16, 45, 28, 37, 1, 19, 2, 25, 14, 0, 22, 6, 17, 35, 24, 11, 12, 27, 42, 49, 32, 13, 20, 23, 43, 41, 30, 4, 9, 21, 3, 10, 34, 38, 15, 18, 5, 47, 39, 44, 40, 8, 7, 31, 48, 46, 29] 。
1 from pyevolve import G1DList
2 from pyevolve import GSimpleGA
3 from pyevolve import Selectors
4 from pyevolve import Statistics
5 from pyevolve import DBAdapters
6 import pyevolve
7 from math import sin
8
9 """ 指定 (0,1,2,.........49 等50個數字不可重複）給 x0~x49，例如 x0=12, x1= 33, ....
10 y = [ sin(1*x0) * sin(2*x1) ] + [ sin(3*x2) * sin(4*x3) ] + ... + [ sin(49*x48) * sin(50*x49) ]
11 求解 y 之 最大值＝?
12 """
13
14 def eval_func(chromosome):
15 score = 20.0 #為了不讓 score 的值小於 0，因為 pyevolve 不支援適存值小於 0 。
16 list = map(lambda a,b: (a, b), xrange(50), chromosome)
17 list.sort(key=lambda a: a[1])
18 for i in xrange(25):
19 score += sin((2*i+1)*list[i*2][0]) * sin((2*i+2)*list[i*2+1][0])
20
21 return score
22
23 # Enable the pyevolve logging system
24 pyevolve.logEnable()
25 # Genome instance, 1D List of 50 elements
26 genome = G1DList.G1DList(50)
27 # Sets the range max and min of the 1D List
28 genome.setParams(rangemin=0, rangemax=500)
29 # The evaluator function (evaluation function)
30 genome.evaluator.set(eval_func)
31 # Genetic Algorithm Instance
32 ga = GSimpleGA.GSimpleGA(genome)
33 # Set the Roulette Wheel selector method, the number of generations and
34 # the termination criteria
35 ga.selector.set(Selectors.GRouletteWheel)
36 ga.setGenerations(5000)
37 ga.terminationCriteria.set(GSimpleGA.ConvergenceCriteria)
38 # Sets the DB Adapter, the resetDB flag will make the Adapter recreate
39 # the database and erase all data every run, you should use this flag
40 # just in the first time, after the pyevolve.db was created, you can
41 # omit it.
42 sqlite_adapter = DBAdapters.DBSQLite(identify="ex1", resetDB=True)
43 ga.setDBAdapter(sqlite_adapter)
44 # Do the evolution, with stats dump
45 # frequency of 20 generations
46 ga.evolve(freq_stats=20)
47 # Best individual
48 print ga.bestIndividual()
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