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#!/usr/bin/env python
#
# Author: Patrick Hung (patrickh @caltech)
# Author: Mike McKerns (mmckerns @caltech and @uqfoundation)
# Copyright (c) 1997-2016 California Institute of Technology.
# Copyright (c) 2016-2024 The Uncertainty Quantification Foundation.
# License: 3-clause BSD. The full license text is available at:
# - https://github.com/uqfoundation/mystic/blob/master/LICENSE
"""
Sets up De Jong's Fourth function. This is problem 4 of testbed 1 in [1].
This is function fitting "with noise."
Reference:
[1] Storn, R. and Price, K. Differential Evolution - A Simple and Efficient
Heuristic for Global Optimization over Continuous Spaces. Journal of Global
Optimization 11: 341-359, 1997.
[2] Storn, R. and Proce, K. Same title as above, but as a technical report.
try: http://www.icsi.berkeley.edu/~storn/deshort1.ps
"""
from mystic.solvers import DifferentialEvolutionSolver
from mystic.termination import ChangeOverGeneration, VTR
from mystic.strategy import Best1Exp, Rand1Exp
from mystic.models.dejong import quartic as DeJong4
from mystic.tools import random_seed
random_seed(123)
ND = 30
NP = 10
MAX_GENERATIONS = 2500
def main():
solver = DifferentialEvolutionSolver(ND, NP)
solver.SetRandomInitialPoints(min = [-1.28]*ND, max = [1.28]*ND)
solver.SetEvaluationLimits(generations=MAX_GENERATIONS)
solver.Solve(DeJong4, termination=VTR(15), strategy=Rand1Exp, \
CrossProbability=0.3, ScalingFactor=1.0)
solution = solver.Solution()
print(solution)
if __name__ == '__main__':
from timeit import Timer
# optimize with DESolver
t = Timer("main()", "from __main__ import main")
timetaken = t.timeit(number=1)
print("CPU Time: %s\n" % timetaken)
# optimize with fmin
from mystic.solvers import fmin
print(fmin(DeJong4, [0 for i in range(ND)]))
# end of file
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