Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.Genetic Algorithms

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    Parameter optimization for GARCH Model using hybrid PSO-GA
    (Institute of Electrical and Electronics Engineers Inc., 2022) Lalitha, R.; Mohan, B.R.
    Building Garch Models using classical numerical methods has been a difficult task, and many approaches to solve this problem has been proposed in the recent past which includes meta heuristic and evolutionary algorithms as well. In addition, the Fluctuations found in stock market follow volatility clustering. This clustering feature has made forecasting difficult. In this paper we plan to estimate the parameters of GARCH Model using PSO-GA approach, and also consider the effects caused by the positive shocks and negative shocks using GJR-Garch. We also propose a comparison between hybrid PSO(Particle Swarm Optimization) and GA(Genetic Algorithm) for parameter optimization in GARCH Model, which will be compared with the existing systems. © 2022 IEEE.
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    Hybrid Genetic Algorithm and Machine Learning Approach for Software Reliability Assessment in Safety-Critical Systems
    (Institute of Electrical and Electronics Engineers Inc., 2024) Goyal, G.; Sharma, K.; Anshuman; Mittal, V.; Singla, B.; Das, M.; Mohan, B.R.
    Software reliability is a paramount determinant of software quality. In this research paper, we delve into utilizing Genetic Algorithms (GAs) for feature selection and classification. We undertake a comprehensive evaluation and comparative analysis of Machine Learning models, specifically Random Forest and Logistic Regression, both with and without Genetic Algorithmdriven feature selection. Our findings substantiate the significant impact of Genetic Algorithms in improving the accuracy of software reliability analysis. © 2024 IEEE.
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    Enhancing Movie Recommendation Systems with MapReduce Genetic Algorithms: Addressing Scalability and Accuracy Challenges
    (Institute of Electrical and Electronics Engineers Inc., 2024) Patidar, P.; Posa, S.V.; Girish, K.K.; Rao, S.; Bhowmik, B.
    In the world of big data, the efficacy of movie recommendation systems is crucial for personalizing user experiences in digital entertainment. Traditional methods, including collaborative and content-based filtering, often encounter limitations such as data sparsity, cold start problems, and scalability issues. This paper introduces a novel approach that integrates MapReduce technology with Genetic Algorithms (GAs) to address these chal-lenges. Utilizing the Hadoop framework, our MapReduce Genetic Algorithm (MRGA) efficiently processes extensive datasets by distributing tasks across a cluster of machines. The genetic algorithm component optimizes recommendation accuracy through advanced techniques like selection, crossover, and mutation. Our experimental results, based on the MovieLens 100K dataset, demonstrate that the MRGA approach outperforms traditional collaborative filtering methods in terms of recommendation accuracy and scalability. By leveraging MapReduce's distributed computing power and the GA's optimization capabilities, this research offers a robust solution to improve movie recommendations and handle large-scale data efficiently. © 2024 IEEE.