2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computi 2015
DOI: 10.1109/uic-atc-scalcom-cbdcom-iop.2015.133
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Research on Evolvable Hardware Based on Population Hybridization Monkey-King Genetic Algorithm

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Cited by 2 publications
(4 citation statements)
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“…Usually, the intrinsic EHW is used because it does not require special modeling software [6], and it can get a more appropriate solution based on the current state of the reconfigurable hardware. The research of evolvable hardware mainly focuses on three aspects [7]: propose more efficient evolutionary Since the output of combinational logic circuit is only related to the current input, the expected function can be realized only when the truth table is fully matched, so using the truth table for fitness evaluation is still the mainstream method. Most researches to improve evolutionary efficiency scholars are usually based on the first two, among which the improvement of evolutionary algorithm is the majority.…”
Section: Introductionmentioning
confidence: 99%
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“…Usually, the intrinsic EHW is used because it does not require special modeling software [6], and it can get a more appropriate solution based on the current state of the reconfigurable hardware. The research of evolvable hardware mainly focuses on three aspects [7]: propose more efficient evolutionary Since the output of combinational logic circuit is only related to the current input, the expected function can be realized only when the truth table is fully matched, so using the truth table for fitness evaluation is still the mainstream method. Most researches to improve evolutionary efficiency scholars are usually based on the first two, among which the improvement of evolutionary algorithm is the majority.…”
Section: Introductionmentioning
confidence: 99%
“…The co-evolution of multiple populations was adopted to obtain faster convergence speed and better evolutionary performance. Reference [7] constructs a virtual reconfigurable circuit based on artificial neural network architecture, and designs a unique coding form for this structure, then an improved simulated annealing algorithm is proposed to accelerate the evolution through the inner and outer loops. In order to expand the scale of evolution, only one row of programmable elements was evolved in [8].…”
Section: Introductionmentioning
confidence: 99%
“…Stomeo, Kalganova, and Lambert presented a scalable GA built on a PLA for the design of digital circuits that could evolve faster than a traditional GA [154]. Another enhanced GA was proposed in [155] to reduce the evolution time and the required computations. Li, Fialho, Kwong, and Zhang proposed an adaptive operator selection method in which the application rate of different genetic operators is determined in an online fashion based on their performance [156].…”
Section: Genetic Algorithmmentioning
confidence: 99%
“…with novel EAs [153,151,155,152,154]. There were no EHW implementations that were constructed with a novel reconfigurable hardware core and novel EA; our proposed system is the first that includes both.…”
Section: Evolvable Hardware Implementationsmentioning
confidence: 99%