An evolutionary optimization algorithm for gradually saturating objective functions

Evolutionary algorithms have been actively studied for dynamic optimization problems in the last two decades, however the research is mainly focused on problems with large, periodical or abrupt changes during the optimization. In contrast, this paper concentrates on gradually changing environments with an additional imposition of a saturating objective function. This work is motivated by an evolutionary neural architecture search methodology where a population of Convolutional Neural Networks (CNNs) is evaluated and iteratively modified using genetic operators during the training process. The objective of the search, namely the prediction accuracy of a CNN, is a continuous and slow moving target, increasing with each training epoch and eventually saturating when the training is nearly complete. Population diversity is an important consideration in dynamic environments wherein a large diversity restricts the algorithm from converging to a small area of the search space while the environment is still transforming. Our proposed algorithm adaptively influences the population diversity, depending on the rate of change of the objective function, using disruptive crossovers and non-elitist population replacements. We compare the results of our algorithm with a traditional evolutionary algorithm and demonstrate that the proposed modifications improve the algorithm performance in gradually saturating dynamic environments.