Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents the extension of framework for automatic design space exploration (FADSE) tool using a meta-optimization approach, which is used to improve the performance of design space exploration algorithms, by driving two different multiobjective meta-heuristics concurrently. More precisely,  selected two genetic multiobjective algorithms: 1) non-dominated sorting genetic algorithm-II and 2) strength Pareto evolutionary algorithm 2, that work together in order to improve both the solutions’ quality and the convergence speed. With the proposed improvements,  ran FADSE in order to optimize the hardware parameters’ values of the grid ALU processor (GAP) micro-architecture from a bi-objective point of view (performance and hardware complexity). Using our developed approach  obtained better GAP instances (a configuration has for almost the same cycles per instruction – 1.00, the hardware complexity 38% smaller/better – 35.81 versus 58.61) in half of the time compared to a classical sequential optimization approach (5 days versus 10 days).
['Lucian N. Vintan', 'Radu Chis', 'Muhammad Ali Ismail', 'Cristian Cotofana']
I am writing a research paper on a idea Improving Computing Systems Automatic Multiobjective Optimization Through Meta-Optimization
[{"from": "human", "value": "I am writing a research paper on a idea Improving Computing Systems Automatic Multiobjective Optimization Through Meta-Optimization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents the extension of framework for automatic design space exploration (FADSE) tool using a meta-optimization approach, which is used to improve the performance of design space exploration algorithms, by driving two different multiobjective meta-heuristics concurrently. More precisely,  selected two genetic multiobjective algorithms: 1) non-dominated sorting genetic algorithm-II and 2) strength Pareto evolutionary algorithm 2, that work together in order to improve both the solutions\u2019 quality and the convergence speed. With the proposed improvements,  ran FADSE in order to optimize the hardware parameters\u2019 values of the grid ALU processor (GAP) micro-architecture from a bi-objective point of view (performance and hardware complexity). Using our developed approach  obtained better GAP instances (a configuration has for almost the same cycles per instruction \u2013 1.00, the hardware complexity 38% smaller/better \u2013 35.81 versus 58.61) in half of the time compared to a classical sequential optimization approach (5 days versus 10 days)."}]