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Prof Andy Tyrrell

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University of York

Andy Tyrrell received a 1st class honours degree in 1982 and a PhD in 1985, both in Electrical and Electronic Engineering.

Andy’s main research interests are in the design of biologically-inspired architectures, computer engineering, microelectronics, robotics, evolvable hardware, FPGA system design, and fault tolerant design. In particular, over the last 20 years his research group at York has concentrated on bio-inspired systems. This work has included the creation of embryonic processing array, intrinsic evolvable hardware systems, including the RISA and PAnDA chips and the immunotronics hardware architecture.

RIED Specific Links & Papers

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  • 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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