Large scale optimization of computationally expensive functions

Falco, Ivanoe De; Cioppa, Antonio Della; Trunfio, Giuseppe A.

doi:10.1145/3067695.3084214

Cited by 8 publications

(1 citation statement)

References 22 publications

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“…According to the literature, several design details, such as the collaboration method and the strategy used for decomposing the problem, can have a significant impact on the performance of a CC optimizer [8,49]. Interestingly, the CC technique enables a natural data-parallel approach, given that the subcomponents can be evolved independently with only periodical exchange of information [7].…”

Section: Introductionmentioning

confidence: 99%

Adaptive cooperative coevolutionary differential evolution for parallel feature selection in high-dimensional datasets

2023

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In many fields, it is a common practice to collect large amounts of data characterized by a high number of features. These datasets are at the core of modern applications of supervised machine learning, where the goal is to create an automatic classifier for newly presented data. However, it is well known that the presence of irrelevant features in a dataset can make the learning phase harder and, most importantly, can lead to suboptimal classifiers. Consequently, it is becoming increasingly important to be able to select the right subset of features. Traditionally, optimization metaheuristics have been used with success in the task of feature selection. However, many of the approaches presented in the literature are not applicable to datasets with thousands of features because of the poor scalability of optimization algorithms. In this article, we address the problem using a cooperative coevolutionary approach based on differential evolution. In the proposed algorithm, parallelized for execution on shared-memory architectures, a suitable strategy for reducing the dimensionality of the search space and adjusting the population size during the optimization results in significant performance improvements. A numerical investigation on some high-dimensional and medium-dimensional datasets shows that, in most cases, the proposed approach can achieve higher classification performance than other state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%