A Hyper-heuristic approach towards mitigating Premature Convergence caused by the objective fitness function in GP

This manuscript proposes a hyper-heuristic approach towards mitigating Premature Convergence caused by objective fitness in Genetic Programming (GP). The objective fitness function used in standard GP has the potential to profoundly exacerbate Premature Convergence in the algorithm. Accordingly several alternative fitness measures have been proposed in GP literature. These alternative fitness measures replace the objective function, with the specific aim of mitigating this type of Premature Convergence. However each alternative fitness measure is found to have its own intrinsic limitations. To this end the proposed approach automates the selection of distinct fitness measures during the progression of GP. The power of this methodology lies in the ability to compensate for the weaknesses of each fitness measure by automating the selection of the best alternative fitness measure. Our hyper-heuristic approach is found to achieve generality in the alleviation of Premature Convergence caused by objective fitness. Vitally the approach is unprecedented and highlights a new paradigm in the design of GP systems.

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