From feature selection to continuous optimization

TL;DR

This paper introduces MaNet, a deep learning-based optimization method leveraging feature selection to efficiently solve large-scale continuous problems, showing competitive results against traditional algorithms.

Contribution

It proposes MaNet, a novel deep learning approach that uses feature selection to improve optimization of massive continuous problems, outperforming some existing algorithms.

Findings

MaNet achieves competitive solution accuracy.

MaNet demonstrates scalability to large problems.

Deep learning can effectively replace traditional metaheuristics.

Abstract

Metaheuristic algorithms (MAs) have seen unprecedented growth thanks to their successful applications in fields including engineering and health sciences. In this work, we investigate the use of a deep learning (DL) model as an alternative tool to do so. The proposed method, called MaNet, is motivated by the fact that most of the DL models often need to solve massive nasty optimization problems consisting of millions of parameters. Feature selection is the main adopted concepts in MaNet that helps the algorithm to skip irrelevant or partially relevant evolutionary information and uses those which contribute most to the overall performance. The introduced model is applied on several unimodal and multimodal continuous problems. The experiments indicate that MaNet is able to yield competitive results compared to one of the best hand-designed algorithms for the aforementioned problems, in terms of the solution accuracy and scalability.