Hardware Genetic Algorithm Optimization by Critical Path Analysis using a Custom VLSI Architecture
This paper propose a Virtual-Field Programmable Gate Array (V-FPGA) architecture that allows direct access to its configuration bits to facilitate hardware evolution, thereby allowing any combinational or sequential digital circuit to be realized. By using the V-FPGA, this paper investigates two pos...
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Published in: | South African computer journal = Suid-Afrikaanse rekenaartydskrif Vol. 56; no. 56 |
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Makhanda
South African Institute of Computer Scientists and Information Technologists
11-07-2015
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Abstract | This paper propose a Virtual-Field Programmable Gate Array (V-FPGA) architecture that allows direct access to its configuration bits to facilitate hardware evolution, thereby allowing any combinational or sequential digital circuit to be realized.
By using the V-FPGA, this paper investigates two possible ways of making evolutionary hardware systems more scalable: by optimizing the system’s genetic algorithm (GA); and by decomposing the solution circuit into smaller, evolvable sub-circuits. GA optimization is done by: omitting a canonical GA’s crossover operator (i.e. by using a 1+λ algorithm); applying evolution constraints; and optimizing the fitness function.
A noteworthy contribution this research has made is the in-depth analysis of the phenotypes’ CPs. Through analyzing the CPs, it has been shown that a great amount of insight can be gained into a phenotype’s fitness.
We found that as the number of columns in the Cartesian Genetic Programming array increases, so the likelihood of an external output being placed in the column decreases. Furthermore, the number of used LEs per column also substantially decreases per added column.
Finally, we demonstrated the evolution of a state-decomposed control circuit. It was shown that the evolution of each state’s sub-circuit was possible, and suggest that modular evolution can be a successful tool when dealing with scalability. |
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AbstractList | This paper propose a Virtual-Field Programmable Gate Array (V-FPGA) architecture that allows direct access to its configuration bits to facilitate hardware evolution, thereby allowing any combinational or sequential digital circuit to be realized. By using the V-FPGA, this paper investigates two possible ways of making evolutionary hardware systems more scalable: by optimizing the system’s genetic algorithm (GA); and by decomposing the solution circuit into smaller, evolvable sub-circuits. GA optimization is done by: omitting a canonical GA’s crossover operator (i.e. by using a 1+λ algorithm); applying evolution constraints; and optimizing the fitness function. A noteworthy contribution this research has made is the in-depth analysis of the phenotypes’ CPs. Through analyzing the CPs, it has been shown that a great amount of insight can be gained into a phenotype’s fitness. We found that as the number of columns in the Cartesian Genetic Programming array increases, so the likelihood of an external output being placed in the column decreases. Furthermore, the number of used LEs per column also substantially decreases per added column. Finally, we demonstrated the evolution of a state-decomposed control circuit. It was shown that the evolution of each state’s sub-circuit was possible, and suggest that modular evolution can be a successful tool when dealing with scalability. This paper propose a Virtual-Field Programmable Gate Array (V-FPGA) architecture that allows direct access to its configuration bits to facilitate hardware evolution, thereby allowing any combinational or sequential digital circuit to be realized. By using the V-FPGA, this paper investigates two possible ways of making evolutionary hardware systems more scalable: by optimizing the system’s genetic algorithm (GA); and by decomposing the solution circuit into smaller, evolvable sub-circuits. GA optimization is done by: omitting a canonical GA’s crossover operator (i.e. by using a 1+λ algorithm); applying evolution constraints; and optimizing the fitness function. A noteworthy contribution this research has made is the in-depth analysis of the phenotypes’ CPs. Through analyzing the CPs, it has been shown that a great amount of insight can be gained into a phenotype’s fitness. We found that as the number of columns in the Cartesian Genetic Programming array increases, so the likelihood of an external output being placed in the column decreases. Furthermore, the number of used LEs per column also substantially decreases per added column. Finally, we demonstrated the evolution of a state-decomposed control circuit. It was shown that the evolution of each state’s sub-circuit was possible, and suggest that modular evolution can be a successful tool when dealing with scalability. |
Author | Smith, Farouk Van den Berg, Allan Edward |
Author_xml | – sequence: 1 givenname: Farouk surname: Smith fullname: Smith, Farouk – sequence: 2 givenname: Allan Edward surname: Van den Berg fullname: Van den Berg, Allan Edward |
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SubjectTerms | Critical path Field programmable gate arrays FPGA, Genetic Algorithm, Virtual Reconfigurable Circuit, Evolutionary Hardware, Logic Element, Digital Circuit, Cartesian Genetic Programming, Hardware Chromosome, Intrinsic Evolution, Off-Chip Evolution, On-Chip Evolution Genetic algorithms |
Title | Hardware Genetic Algorithm Optimization by Critical Path Analysis using a Custom VLSI Architecture |
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