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import numpy as np |
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from gradient_free_optimizers import ( |
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HillClimbingOptimizer, |
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StochasticHillClimbingOptimizer, |
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RepulsingHillClimbingOptimizer, |
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RandomSearchOptimizer, |
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RandomRestartHillClimbingOptimizer, |
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RandomAnnealingOptimizer, |
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SimulatedAnnealingOptimizer, |
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ParallelTemperingOptimizer, |
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ParticleSwarmOptimizer, |
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EvolutionStrategyOptimizer, |
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BayesianOptimizer, |
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TreeStructuredParzenEstimators, |
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DecisionTreeOptimizer, |
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EnsembleOptimizer, |
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) |
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# check if there are any debug-prints left in code |
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optimizer_list = [ |
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HillClimbingOptimizer, |
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StochasticHillClimbingOptimizer, |
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RepulsingHillClimbingOptimizer, |
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RandomSearchOptimizer, |
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RandomRestartHillClimbingOptimizer, |
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RandomAnnealingOptimizer, |
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SimulatedAnnealingOptimizer, |
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ParallelTemperingOptimizer, |
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ParticleSwarmOptimizer, |
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EvolutionStrategyOptimizer, |
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BayesianOptimizer, |
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TreeStructuredParzenEstimators, |
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DecisionTreeOptimizer, |
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EnsembleOptimizer, |
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] |
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def objective_function(para): |
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score = -para["x1"] * para["x1"] |
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return score |
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search_space = { |
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"x1": np.arange(0, 5, 1), |
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} |
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for optimizer in optimizer_list: |
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opt0 = optimizer(search_space) |
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opt0.search(objective_function, n_iter=15, verbosity=False, memory=False) |
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opt1 = optimizer(search_space) |
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opt1.search(objective_function, n_iter=15, verbosity=False) |
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opt2 = optimizer(search_space) |
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opt2.search( |
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objective_function, n_iter=15, verbosity=False, memory_warm_start=opt1.results |
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) |
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opt3 = optimizer(search_space, initialize={"warm_start": [{"x1": 1}]}) |
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opt3.search( |
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objective_function, |
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n_iter=15, |
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verbosity=False, |
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) |
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SimonBlanke /
Gradient-Free-Optimizers
