Feb 19, 2021 · In this paper, we explore two evolutionary algorithms: particle swarm optimization and genetic algorithm, for the purposes of performing the choice of optimal hyperparameter values in an autonomous manner.

Mar 23, 2023 · In particular, the paper studies Bayesian optimization in depth and proposes the use of genetic algorithm, differential evolution and covariance matrix adaptation—evolutionary strategy for...

Nov 15, 2015 · To address this, Multi-node Evolutionary Neural Networks for Deep Learning (MENNDL) is proposed as a method for automating network selection on computational clusters through hyper-parameter optimization performed via genetic algorithms.

Oct 1, 2022 · We present a comprehensive global sensitivity analysis of two single-objective and two multi-objective state-of-the-art global optimization evolutionary algorithms as an algorithm configuration problem.

Jul 13, 2021 · It gives practical recommendations regarding important choices to be made when conducting HPO, including the HPO algorithms themselves, performance evaluation, how to combine HPO with ML pipelines, runtime improvements, and parallelization.

In this paper, we explore two evolutionary algorithms: particle swarm optimization and genetic algorithm, for the purposes of performing the choice of optimal hyperparameter values in an autonomous manner. Both of these algorithms will be tested on different datasets and compared to alternative methods.

In this paper, we present a novel and efficient hyperparameter optimization strategy based on a genetic algorithms variant: Biased Random-key Genetic Algorithms (BRKGA). One of the main challenges of BRKGA is its limited capacity to explore the …

Dec 11, 2020 · In this paper, we propose a surrogate-assisted evolutionary algorithm (EA) for hyperparameter optimization of machine learning (ML) models.

Jan 16, 2023 · After a general introduction of hyperparameter optimization, we review important HPO methods such as grid or random search, evolutionary algorithms, Bayesian optimization, Hyperband and racing. We include many practical recommendations w.r.t. performance evaluation, how to combine HPO with ML pipelines, runtime improvements and parallelization.

We present hyper-parameter optimization results on tasks of training neu-ral networks and deep belief networks (DBNs). We optimize hyper-parameters using random search and two new greedy sequential methods based on the ex-pected improvement criterion.