  In this paper, we scale evolutionary algorithms to high-dimensional
optimization problems that deceptively possess a low effective dimensionality
(certain dimensions do not significantly affect the objective function). To
this end, an instantiation of the multiform optimization paradigm is presented,
where multiple low-dimensional counterparts of a target high-dimensional task
are generated via random embeddings. Since the exact relationship between the
auxiliary (low-dimensional) tasks and the target is a priori unknown, a
multiform evolutionary algorithm is developed for unifying all formulations
into a single multi-task setting. The resultant joint optimization enables the
target task to efficiently reuse solutions evolved across various
low-dimensional searches via cross-form genetic transfers, hence speeding up
overall convergence characteristics. To validate the overall efficacy of our
proposed algorithmic framework, comprehensive experimental studies are carried
out on well-known continuous benchmark functions as well as a set of practical
problems in the hyper-parameter tuning of machine learning models and deep
learning models in classification tasks and Predator-Prey games, respectively.
