FBM

Flexible budget algorithm used to demonstrate the Flexible budget method, based upon Branke, J. and Elomari, J. (2012). Meta-optimization for parameter tuning with a flexible computing budget. In Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Conference, pages 1245–1252. ACM.

Changes made

• focuses specified OFE budgets
• gaussian_mutation std control parameter added

Branke, J. and Elomari, J. (2012) says the rank-tie breakers of AUC (area under the curve), and AL (area lost) are essientially the same, so only implemented AUC.

class optTune.FBA_code.FBA(optAlg, CPV_lb, CPV_ub, CPV_validity_checks, OFE_budgets, repeats, gammaBudget, N, crossover_rate=0.5, mutation_strength=0.1, tiebreaking_method='AUC', printFunction=<function to_stdout at 0x2b1de8149c08>, printLevel=2, saveTo=None, addtoBatch=<function _passfunction at 0x2b1de8149578>, processBatch=<function _passfunction at 0x2b1de8149578>)

Parameters * optAlg - algorithm being tuned, where optAlg(CPV_tuple, OFE_budgets, randomSeed) returns F,E curve or history * CPV_lb - control parameter value lower bounds * CPV_ub - control parameter values upper bounds * CPV_validity_checks - CPV_validity_checks( CPV_array, OFE_budget ) return (Valid,msg) * gammaBudget - gamma = optAlg_eval made, i.e. running the optAlg 3 times, using OFE budget of 40, results in a gamma of 120.

Example

import numpy
from scipy import optimize
from optTune import FBA, evaluation_history_recording_wrapper

def Ros_ND(x) :
    "gerneralised Rossenbroch function"
    return sum([100*(x[ii+1]-x[ii]**2)**2 + (1-x[ii])**2 for ii in range(len(x)-1)])

def solution_valid(x):
    return ( -2.048 <= x ).all() and ( x <= 2.048 ).all()

def run_simulated_annealing(CPVs, OFE_budgets, randomSeed):
    dwell = int(CPVs[0]) #equibavent to population size in evolutionary algorithms
    func = evaluation_history_recording_wrapper( Ros_ND, dwell, solution_valid )
    optimize.anneal(func, 
                    x0 = -0.5 * numpy.random.rand(5),
                    #x0 = -2.048 + 2*2.048*numpy.random.rand(10), #if used make sure tMOPSO sample size greater than 100
                    m = CPVs[1],
                    T0 = 500.0,
                    lower= -2.048,
                    upper=  2.048,
                    dwell=dwell, 
                    maxeval = max( OFE_budgets ), #termination criteria
                    feps = 0.0, 
                    Tf = 0.0)
    return func.f_hist, func.OFE_hist

def CPV_valid(CPVs, OFE_budget):
    if CPVs[0] < 5:
        return False,'dwell,CPVs[0] < 5'
    if CPVs[1] < 0.0001:
        return False,'CPVs[1] < 0.0001'
    return True,''

tuningOpt = FBA( 
    optAlg = run_simulated_annealing, 
    CPV_lb = numpy.array([10, 0.0]), 
    CPV_ub = numpy.array([50, 5.0]),
    CPV_validity_checks = CPV_valid,
    OFE_budgets=numpy.logspace(1,3,30).astype(int),
    repeats = 30,
    gammaBudget = 30*1000*50, #increase to get a smoother result ...
    N = 10,
    )
print(tuningOpt)

Fmin_values = tuningOpt.combined_curves
OFE_budgets = tuningOpt.OFE_budgets
X_opt = tuningOpt.get_optimal_X()
dwell_values =  X_opt[:,0]
m_values =      X_opt[:,1]

print('OFE budget     Fmin      dwell       m     ')
for a,b,c,d in zip(OFE_budgets, Fmin_values, dwell_values, m_values):
    print('  %i       %6.4f      %i       %4.2f' % (a,b,c,d))

from matplotlib import pyplot
p1 = pyplot.semilogx(OFE_budgets, dwell_values, 'g.')[0]
pyplot.ylabel('dwell')
pyplot.ylim( min(dwell_values) - 1, max( dwell_values) + 1)
pyplot.twinx()
p2 = pyplot.semilogx(OFE_budgets, m_values, 'bx')[0]
pyplot.ylim( 0, max(m_values)*1.1)
pyplot.ylabel('m (rate of cool)')
pyplot.legend([p1,p2],['dwell','m'], loc='upper center')
pyplot.xlabel('OFE budget')
pyplot.xlim(min(OFE_budgets)-1,max(OFE_budgets)+60)
pyplot.title('Optimal CPVs for different OFE budgets')

pyplot.show()