Dr Rahul Dubey

RIED Alumni

Research Fellow

University of York

Our Team > RIED Alumni > Dr Rahul Dubey

Rahul worked on the RIED Project from July 2021 to January 2024 as a Post Doctorate Research Associate until he was promoted to his current position, working as an Assistant Professor of Computer Science at the Missouri State University in the United States

Earlier, Rahul completed his PhD in 2021 from the University of Nevada Reno USA in Computer Science, and his Master’s in Electrical Engineering from National Institute of Technology Kurukshetra India in 2013. In his PhD, he worked on distributed control of heterogeneous agents in dynamic environments and used evolutionary algorithms to evolved robust solutions. Specifically, he worked on on-demand wireless network deployment, game AI, and autonomous surface vessel’s path planning. During his Master’s he worked on evolutionary design of solar power plants to minimize cost of energy production.

His research interests are in the areas of evolutionary computing, machine learning, and their applications in real-world problems. Rahul joined RIED in July 2021 and focused on how to effectively use different evolutionary algorithms and machine learning techniques to evolve organisms/structures, and lattices. He is also interested in Explainable-AI (XAI) and currently looking in how to find the best (or better) algorithms that help in XAI.

He can be contacted either at RahulDubey@MissouriState.edu or rahul.dubey@york.ac.uk

RIED Specific Links & Papers

  • Local Fitness Landscape Exploration Based Genetic Algorithms (January 2023)

    Genetic algorithms (GAs) have been used to evolve optimal/sub-optimal solutions of many problems. When using GAs for evolving solutions, often fitness evaluation is the most computationally expensive, and this discourages researchers from applying GAs for computationally challenging problems. This paper presents an approach for generating offspring based on a local fitness landscape exploration to increase the speed of the search for optimal/sub-optimal solutions and to evolve better fitness solutions. The proposed algorithm, “Fitness Landscape Exploration based Genetic Algorithm” (FLEX-GA) can be applied to single and multi-objective optimization problems. Experiments were conducted on several single and multi-objective benchmark problems with and without constraints. The performance of the FLEX-based algorithm on single-objective problems is compared with a canonical GA and other algorithms. For multi-objective benchmark problems, the comparison is made with NSGA-II, and other multi-objective optimization algorithms. Lastly, Pareto solutions are evolved on eight real-world multi-objective optimization problems, and a comparative performance is presented with NSGA-II. Experimental results show that using FLEX on most of the single and multi-objective problems, the speed of the search improves up to 50% and the quality of solutions also improves. These results provide sufficient evidence of the applicability of fitness landscape approximation-based algorithms for solving real-world optimization problems.

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  • Evolving Design Modifiers (December 2022)

    Evolutionary Developmental biology (EvoDevo) is a process of directed growth whose mechanisms could be used in an evolutionary algorithm for engineering applications. Engineering design can be thought of as a search through a high-dimensional design space for a small number of solutions that are optimal by various metrics. Configuring this search within an EvoDevo algorithm may allow developmental processes to provide a facility to give more immediate, localised feedback to the system as it grows into its final optimal configuration (form). This approach would augment current design practices. The main components needed to run EvoDevo for engineering design are set out in this paper, and these are developed into an algorithm for initial investigations, resulting in evolved neural network-based structural design modifying operators that optimise the structure of a planar truss in an iterative, decentralized manner against multiple objectives. Preliminary results are presented which show that the two levels feedback at the Evo and Devo stages drive the system to ultimately produce feasible solutions.

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