Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Biswas, Anushka; John Joshua, H.; Kalpana, P.; Vasanth, U.; Ramakrishna Gondkar, Raju; Jose, Teena Title A name given to the resource A Hybrid Genetic Algorithm and Large Language Model Approach for Agricultural Products Price Optimization Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2nd International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICECCC65144.2025.11064235" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICECCC65144.2025.11064235</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105012122151?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105012122151?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Biswas A., Christ University, Dept. of Computer Science, Karnataka, Bangalore, India; John Joshua H., Christ University, Dept. of Computer Science, Karnataka, Bangalore, India; Kalpana P., Christ University, Dept. of Computer Science, Karnataka, Bangalore, India; Vasanth U., Christ University, Dept. of Computer Science, Karnataka, Bangalore, India; Ramakrishna Gondkar R., Christ University, Dept. of Computer Science, Karnataka, Bangalore, India; Jose T., Christ University, Dept. of Computer Science, Karnataka, Bangalore, India Description An account of the resource This paper introduces a hybrid approach, based on Genetic Algorithm (GA) and Large Language Models (LLMs), namely Mixtral 8x7B, to optimize pricing strategies for agricultural products. The method processes real-time market data, using Machine Learning (ML) techniques to generate competitive and profitable price recommendations. GAs allow for adaptive optimization, while LLMs capture complex trends in the market, making this approach more precise with respect to the pricing strategy. Case studies related to onions and tomatoes illustrate the efficiency of the optimization process. The outcome shows that the optimized prices achieve a fitness score of 0.915 (onions) and a competitive index of 0.89 (onions) compared to the market averages. Compared to traditional methods, the proposed hybrid model provides a better approach towards decision making through multi-objective optimization and real-time data analysis. This research contributes to improved profitability for farmers by adopting sustainable pricing strategies and agricultural market efficiency. 2025 IEEE. Subject The topic of the resource Agricultural Price Optimization; Farm Profitability; Genetic Algorithms; Large Language Models; Machine Learning; Market Efficiency; Mixtral 8x7B; Sustainable Agriculture Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152162-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online