Time is On The Side of Grammatical Evolution

Murphy, Anthony; Youssef, Ayman; Gupt, Krishn Kumar; Raja, Muhammad Adil; Ryan, Conor

doi:10.1109/iccci50826.2021.9402392

Cited by 6 publications

(6 citation statements)

References 9 publications

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“…Another interesting course of action is to try other techniques to reduce the size of the trees, such as regularization or encapsulation [ 28 ], or hybridise GE with another technique to optimise smaller models already found [ 29 ]. Furthermore, the use of different types or sets of grammar could be investigated.…”

Section: Discussionmentioning

confidence: 99%

Fuzzy Pattern Tree Evolution Using Grammatical Evolution

et al. 2022

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A novel approach to induce Fuzzy Pattern Trees using Grammatical Evolution is presented in this paper. This new method, called Fuzzy Grammatical Evolution, is applied to a set of benchmark classification problems. Experimental results show that Fuzzy Grammatical Evolution attains similar and oftentimes better results when compared with state-of-the-art Fuzzy Pattern Tree composing methods, namely Fuzzy Pattern Trees evolved using Cartesian Genetic Programming, on a set of benchmark problems. We show that, although Cartesian Genetic Programming produces smaller trees, Fuzzy Grammatical Evolution produces better performing trees. Fuzzy Grammatical Evolution also benefits from a reduction in the number of necessary user-selectable parameters, while Cartesian Genetic Programming requires the selection of three crucial graph parameters before each experiment. To address the issue of bloat, an additional version of Fuzzy Grammatical Evolution using parsimony pressure was tested. The experimental results show that Fuzzy Grammatical Evolution with this extension routinely finds smaller trees than those using Cartesian Genetic Programming without any compromise in performance. To improve the performance of Fuzzy Grammatical Evolution, various ensemble methods were investigated. Boosting was seen to find the best individuals on half the benchmarks investigated.

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Section: Discussionmentioning

confidence: 99%

Fuzzy Pattern Tree Evolution Using Grammatical Evolution

et al. 2022

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“…Each list corresponds to a specific non-terminal and as long as each production in the non-terminal has the same arity swapping these productions will not change the structure of the phenotype ensuring that the shape created by PTC2 is undisturbed. Hybrid optimisation has already been successfully used in GE to optimise constant values within an individual [17,12]. Here we use it to optimise functions, variable or constants and do not optimise list corresponding to non-terminals which would alter the structure.…”

Section: Local Optimised Probabilistic Tree Creation 2 (Ptc2)mentioning

confidence: 99%

An investigation into Structured Grammatical Evolution Initialisation

Murphy,

Mahdinejad,

Ventresque

et al. 2024

Preprint

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A key ingredient in any successful genetic programming is robust initialisation. Many successful initialisation methods used in genetic programming have been adapted to use with grammatical evolution, to varying levels success. This papaer examines the effectiveness of some of the most popular of these initialisation techniques on structured grammatical evolution. Namely, we investigate Sensible Initialisation and Probabilistic Tree Creation 2, as well as the standard initialisation procedure used in structured grammatical evolution, Grow. We also propose a novel procedure called Local Optimised Probabilistic Tree Creation 2, which runs a quick greedy optimisation on the trees created. We examine their performance, as well as the diversity of solutions they create, on 7 well-known benchmarks. We observe that Local Optimised Probabilistic Tree Creation 2 created the fittest, or joint fittest, initialisation populations on every benchmark considered. This did not necessarily result in overall better runs, however, and SGE runs with below average initialisation performance were seen to overcome their ''bad start". The diversity of solutions, particularly fitness diversity, at the end of the run was lower for Local Optimised Probabilistic Tree Creation 2 and Probabilistic Tree Creation 2 than for both sensible initialisation and grow.

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“…GELAB was initially tested against data related to speech quality estimation [12], [13]. Apart from that, GELAB was also tested against various well-known benchmark problems related to symbolic regression [14]- [16].…”

Section: Gelabmentioning

confidence: 99%

“…These polynomials are shown in Table 1. These have been used in past works such as those reported on in [14]- [16], [23]. We also compared the results of GELAB with GP.…”

Section: B Performance Analysis Of Gelabmentioning

confidence: 99%

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GELAB – The Cutting Edge of Grammatical Evolution

et al. 2022

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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.

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Time is On The Side of Grammatical Evolution

Cited by 6 publications

References 9 publications

Fuzzy Pattern Tree Evolution Using Grammatical Evolution

Fuzzy Pattern Tree Evolution Using Grammatical Evolution

An investigation into Structured Grammatical Evolution Initialisation

GELAB – The Cutting Edge of Grammatical Evolution

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