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.

The computational complexity of Evolutionary Algorithms (EAs) is a well-known concern. This paper is concerned with the resource consumption of GELAB, a novel Grammatical Evolution (GE) system implemented in Matlab. GE is an evolutionary technique for program search that manipulates large populations of computer programs over multiple generations. In this paper, we present our reflections on some recently conducted experiments with GELAB. GELAB leverages the power of Matlab to perform hybrid optimization on its individuals. The results show that we can substantially decrease computation time without compromising the accuracy of the system.

Over the past seven decades since the advent of artificial intelligence (AI) technology, researchers have demonstrated and deployed systems incorporating AI in various domains. The absence of model explainability in critical systems such as medical AI and credit risk assessment among others has led to neglect of key ethical and professional principles which can cause considerable harm. With explainability methods, developers can check their models beyond mere performance and identify errors. This leads to increased efficiency in time and reduces development costs. The article summarizes that steering the traditional AI systems toward responsible AI engineering can address concerns raised in the deployment of AI systems and mitigate them by incorporating explainable AI methods. Finally, the article concludes with the societal benefits of the futuristic AI systems and the market shares for revenue generation possible through the deployment of trustworthy and ethical AI systems.

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.

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 Algorithms (EAs), 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 Evolutionary Algorithms (EAs) to utilize training data adaptively and effectively. Our objective is to achieve comparable circuit solutions within a reduced run-time. 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, run-time efficiency, and performance parameters evaluated by the Cadence Genus Synthesis solution tool.