Hideyuki Sugiura

Papers

Total Citations

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About

Hideyuki Sugiura is a researcher at the forefront of applying evolutionary computation to financial forecasting. His work centers on grammatical evolution (GE), a powerful evolutionary computation technique that automatically discovers optimal functions or executable programs for complex design objectives. Sugiura’s most notable contribution is the application of GE to stock price prediction, demonstrating how evolutionary algorithms can uncover hidden patterns in volatile financial data. His 2020 paper on this topic, which has garnered 4 citations, showcases a novel approach to generating predictive models that adapt to market dynamics. Beyond finance, Sugiura’s research explores how GE can solve broader optimization problems, including robot control algorithms. By bridging evolutionary computation and practical forecasting, he offers a fresh perspective on data-driven decision-making. His work is particularly valuable for students and researchers interested in the intersection of artificial intelligence, evolutionary algorithms, and quantitative finance, highlighting the potential of grammatical evolution to generate interpretable and effective solutions in real-world applications.