Repetitive Processes and Their Surrogate-Model Congruent Encoding for Evolutionary Algorithms - A Theoretic Proposal

Evolutionary algorithms are a well-known optimisation technique. They can handle very different optimisation tasks and deal with distorted search spaces as well as non-differentiable optimisation functions. One crucial aspect in the design of evolutionary algorithms is the choice of encoding. Especially its interplay with the other components of the evolutionary algorithm is a relevant factor for the success of an evolutionary algorithm. While some encoding situations are relatively trivial, others pose a challenge. We focus on encoding repetitive processes, i.e. processes that consist of several variations of the same basic process (only with varied parameters). Our work proposes a possible technique that enables the validity of the encoded search space. We also provide adaptions to the standard operators of evolutionary algorithms to ensure they produce valid solutions. Furthermore, we show how this encoding technique is compatible with using a surrogate function for the fitness calculation and may reduce the necessary training data.