TY - JOUR
T1 - GELAB - The Cutting Edge of Grammatical Evolution
AU - Gupt, Krishn Kumar
AU - Youssef, Ayman
AU - Murphy, Aidan
AU - Raja, Muhammad Adil
AU - Ryan, Conor
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2022
Y1 - 2022
N2 - The advent of cloud-based super-computing platforms has given rise to a Data Science (DS) boom. Many types of technological problems that were once considered prohibitively expensive to tackle are now candidates for exploration. Machine Learning (ML) tools that were valued only in academic environments are quickly being embraced by industrial giants and tiny startups alike. Coupled with modern-day computing power, ML tools can be looked at as hammers that can deal with even the most stubborn nails. ML tools have become so ubiquitous that the current industrial expectation is that they should not only deliver accurate and intelligent solutions but also do so rapidly. In order to keep pace with these requirements, a new enterprise, referred to as MLOps has blossomed in recent years. MLOps combines the process of ML and DS with an agile software engineering technique to develop and deliver solutions in a fast and iterative way. One of the key challenges to this is that ML and DS tools should be efficient and have better usability characteristics than were traditionally offered. In this paper, we present a novel software for Grammatical Evolution (GE) that meets both of these expectations. Our tool, GELAB, is a toolbox for GE in Matlab which has numerous features that distinguish it from existing contemporary GE software. Firstly, it is user-friendly and its development was aimed for use by non-specialists. Secondly, it is capable of hybrid optimization, in which standard numerical optimization techniques can be added to GE. We have shown experimentally that when hybridized with meta-heuristics GELAB has an overall better performance as compared with standard GE.
AB - The advent of cloud-based super-computing platforms has given rise to a Data Science (DS) boom. Many types of technological problems that were once considered prohibitively expensive to tackle are now candidates for exploration. Machine Learning (ML) tools that were valued only in academic environments are quickly being embraced by industrial giants and tiny startups alike. Coupled with modern-day computing power, ML tools can be looked at as hammers that can deal with even the most stubborn nails. ML tools have become so ubiquitous that the current industrial expectation is that they should not only deliver accurate and intelligent solutions but also do so rapidly. In order to keep pace with these requirements, a new enterprise, referred to as MLOps has blossomed in recent years. MLOps combines the process of ML and DS with an agile software engineering technique to develop and deliver solutions in a fast and iterative way. One of the key challenges to this is that ML and DS tools should be efficient and have better usability characteristics than were traditionally offered. In this paper, we present a novel software for Grammatical Evolution (GE) that meets both of these expectations. Our tool, GELAB, is a toolbox for GE in Matlab which has numerous features that distinguish it from existing contemporary GE software. Firstly, it is user-friendly and its development was aimed for use by non-specialists. Secondly, it is capable of hybrid optimization, in which standard numerical optimization techniques can be added to GE. We have shown experimentally that when hybridized with meta-heuristics GELAB has an overall better performance as compared with standard GE.
KW - Grammatical evolution
KW - diversity
KW - hybrid optimization
UR - http://www.scopus.com/inward/record.url?scp=85128281046&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2022.3166115
DO - 10.1109/ACCESS.2022.3166115
M3 - Article
AN - SCOPUS:85128281046
SN - 2169-3536
VL - 10
SP - 38694
EP - 38708
JO - IEEE Access
JF - IEEE Access
ER -