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The obtained Pareto solutions for every iteration will be saved in a folder for your consideration. For changing the proposed functions for optimisation, check function.py. Accordingly, parameters.py needs to be updated too: INPUT_DIM = # of input dimensions OUTPUT_DIM = # of output dimensions INITIAL = # of initial observations MAXSAMPLE = 10**6 (parameter for inside optimiser of BO, recommended not to be changed) COUNTER = Maximum iterations RPT = # repeating the experiment LEN_SCALE = initial value of length scale for SE kernel VARIANCE_ = initial value of sigma for SE kernel To run the code: python main.py To see the obtained results: python AnalyseMe.py [#of iterations] [#of re-running experiment] [boolean value for detailed plot] Example: python AnalyseMe.py 500 50 0 ** You do not need to stop the optimisation to check out the results. All the obtained results will be saved for every iteration. **
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Cost-Aware Multi-Objective Bayesian Optimisation
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