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11 May 2020 An FPGA implementation of a self-adaptive genetic algorithm
Nonel Thirer
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Proceedings Volume 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II; 114131Y (2020) https://doi.org/10.1117/12.2556789
Event: SPIE Defense + Commercial Sensing, 2020, Online Only
Abstract
A genetic algorithm (GA) is an iterative procedure which performs several processes with the population individuals (chromosomes) to produce a new population, like in the biological evolution. To avoid the premature convergence, the paper proposes a self-adaptive algorithm, which adjusts parameters at the chromosome level and also at the population level, to solve a gender-based GA. Because the FPGA implementation of a self-adaptive GA requires more complicated logic units as for a conventional GA implementation, we propose to optimize this implementation by using a soft or hard processor embedded in the FPGA chip. Thus a part of the tasks will be solved by hardware blocks and a part of the tasks will be solved by the processor.
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Nonel Thirer "An FPGA implementation of a self-adaptive genetic algorithm", Proc. SPIE 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, 114131Y (11 May 2020); https://doi.org/10.1117/12.2556789
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KEYWORDS
Field programmable gate arrays
Genetic algorithms
Evolutionary algorithms
Algorithm development
Data acquisition
Data processing
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