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Published in:
2013 | OriginalPaper | Chapter
7. Adding the “Stochastic Hill-Climber” Learning Algorithm
Author : Gene I. Sher
Published in: Handbook of Neuroevolution Through Erlang
Publisher: Springer New York
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Abstract
In this chapter we discuss the functionality of an optimization method called the Stochastic Hill Climber, and the Stochastic Hill Climber With Random Restarts. We then implement this optimization algorithm, allowing the exoself process to train and optimize the neural network it is overlooking. Afterwards, we implement a new problem interfacing method through the use of public and private scapes, which are simulated environments, not necessarily physical. We apply the new system to the XOR emulation problem, testing its performance on it. Finally, looking to the future and the need for us to be able to test and benchmark our neuroevolutionary system as we add new features to it, we create the benchmarker process, which summons the trainer and the NN it trains, applying it to some specified problem X number of times. Once the benchmarker has applied the trainer to the problem X number of times and accumulated the resulting statistics, it calculates the averages and the associated standard deviations for the important performance parameters of the benchmark.