FlexGP.py: Prototyping Flexibly-Scaled, Flexibly-Factored Genetic Programming for the Cloud

Running genetic programming on the cloud presents researchers with great opportunities and challenges. We argue that standard island algorithms do not have the properties of elasticity and robustness required to run well on the cloud. We present a prototyped design for a decentralized, heterogeneous, robust, self-scaling, self-factoring, self-aggregating genetic programming algorithm. We investigate its properties using a software “sandbox”.

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