GELAB – The Cutting Edge of Grammatical Evolution

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

doi:10.1109/access.2022.3166115

Cited by 6 publications

(1 citation statement)

References 33 publications

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“…GE excels at exploring diverse solution spaces, making it suitable for complex optimization problems. Its versatility and power have led to applications in various fields, including function optimization, circuit design [19,20], symbolic regression [21][22][23][24], time series forecasting, etc. where automated program synthesis and optimization are crucial.…”

Section: Bnf Grammarmentioning

confidence: 99%

Self-Adaptive Learning Using Test Case Selection in Grammatical Evolution for Automatic Design of Digital Circuit

Gupt,

Kshirsagar,

Sarma

et al. 2024

Preprint

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In today’s digital age, the demand for increasingly complex digital circuits spans applications ranging from watches to spacecraft. To meet this demand, circuit design automation using Artificial Intelligence (AI) has emerged as a key solution due to manual processes’ time-consuming and error-prone nature. Among AI techniques, Evolutionary Algorithm (EA), notably Grammatical Evolution(GE), have shown promise. However, the computational cost of these algorithms remains a significant challenge, particularly concerning scalability and the high volume of test cases required for circuit design and synthesis. Balancing the trade-off between the necessity for extensive training data and the computational overhead of GE is crucial. The exponential increase in data volume with circuit complexity presents a challenging obstacle. While utilizing complete training data may ensure accurate circuit design, practical constraints often limit its feasibility. Furthermore, reducing data through selection risks compromising crucial information, while using complete data incurs prohibitively high training costs. This study proposes a novel self-adaptive learning approach within the framework of EAs to utilize training data adaptively and effectively. Our objective is to achieve comparable circuit solutions within a reduced runtime. We apply this technique to GE, naming it Self-adaptive Learning in GE (SLIG). SLIG divides the training data into subsets based on decreasing diversity, ensuring that the model is exposed to highly diverse scenarios early in the evolutionary process and gradually incorporating less diverse data. This method is rigorously evaluated across four digital circuits. The circuits are designed using Hardware Description Language (HDL), facilitating precise and efficient design, simulation, and synthesis. Experimental and statistical results indicate its effectiveness in producing comparable or superior results compared to standard GE, as measured in terms of circuits’ test scores, solution size, runtime efficiency, and performance parameters evaluated by the Cadence Genus Synthesis solution tool.

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Section: Bnf Grammarmentioning

confidence: 99%

Self-Adaptive Learning Using Test Case Selection in Grammatical Evolution for Automatic Design of Digital Circuit

Gupt,

Kshirsagar,

Sarma

et al. 2024

Preprint

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Intensification Process Applied to Codon Selection in Grammatical Evolution

Perez-Rocha,

Carpio,

Rojas-Domínguez

et al. 2024

Studies in Computational Intelligence

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MLOps Challenges in Industry 4.0

Faubel,

Schmid,

Eichelberger

2023

SN COMPUT. SCI.

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An important part of the Industry 4.0 vision is the use of machine learning (ML) techniques to create novel capabilities and flexibility in industrial production processes. Currently, there is a strong emphasis on MLOps as an enabling collection of practices, techniques, and tools to integrate ML into industrial practice. However, while MLOps is often discussed in the context of pure software systems, Industry 4.0 systems received much less attention. So far, there is only little research focusing on MLOps for Industry 4.0. In this paper, we discuss whether MLOps in Industry 4.0 leads to significantly different challenges compared to typical Internet systems. We provide an initial analysis of MLOps approaches and identify both context-independent MLOps challenges (general challenges) as well as challenges particular to Industry 4.0 (specific challenges) and conclude that MLOps works very similarly in Industry 4.0 systems to pure software systems. This indicates that existing tools and approaches are also mostly suited for the Industry 4.0 context.

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GELAB – The Cutting Edge of Grammatical Evolution

Cited by 6 publications

References 33 publications

Self-Adaptive Learning Using Test Case Selection in Grammatical Evolution for Automatic Design of Digital Circuit

Self-Adaptive Learning Using Test Case Selection in Grammatical Evolution for Automatic Design of Digital Circuit

Intensification Process Applied to Codon Selection in Grammatical Evolution

MLOps Challenges in Industry 4.0

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