Implementation of hardware genetic algorithm
Mahmoud, I., Salama, M., Abdel Tawab, A. (2008). Implementation of hardware genetic algorithm. EKB Journal Management System, 6(6th International Conference on Electrical Engineering ICEENG 2008), 1-14. doi: 10.21608/iceeng.2008.34332
Imbaby I. Mahmoud; May Salama; Asmaa Abdel Tawab. "Implementation of hardware genetic algorithm". EKB Journal Management System, 6, 6th International Conference on Electrical Engineering ICEENG 2008, 2008, 1-14. doi: 10.21608/iceeng.2008.34332
Mahmoud, I., Salama, M., Abdel Tawab, A. (2008). 'Implementation of hardware genetic algorithm', EKB Journal Management System, 6(6th International Conference on Electrical Engineering ICEENG 2008), pp. 1-14. doi: 10.21608/iceeng.2008.34332
Mahmoud, I., Salama, M., Abdel Tawab, A. Implementation of hardware genetic algorithm. EKB Journal Management System, 2008; 6(6th International Conference on Electrical Engineering ICEENG 2008): 1-14. doi: 10.21608/iceeng.2008.34332

Implementation of hardware genetic algorithm

The International Conference on Electrical Engineering
Article 86, Volume 6, 6th International Conference on Electrical Engineering ICEENG 2008, May 2008, Page 1-14  XML PDF (887.87 K)
Document Type: Original Article
DOI: 10.21608/iceeng.2008.34332
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Authors
Imbaby I. Mahmoud1; May Salama2; Asmaa Abdel Tawab1
1Atomic Energy Auority, Cairo.
2Shobra Faculty of Engineering, Benha Univ. Cairo.
Abstract
Abstract:
This work presents a hardware implementation of a Genetic Algorithm. Hardware
Genetic Operators are implemented in FPGA. Fitness evaluation, which is problem
dependent, is left for implementation as S/W module or problem specific hardware
design. This allowed a re-configurable general-purpose design, which is customized by
application specific population generation and fitness evaluation solution. A 16 site
Random Number Generator module is implemented in VHDL based on Hybrid Cellular
Automata (CA). Selection, Crossover, and Mutation Operators are implemented as
systolic architecture. For preserving locality & modularity of systolic arrays we separate
selection array implementation from the crossover and mutation operators. The
chromosomes are fed serially to allow variable length chromosomes. The Genetic
Engine is targeted a Xilinx Vertex XC2V2000-5 device using Xilinx Foundation
Environment. The simulation is carried out using ModelSim.
Keywords
Genetic Algorithms; FPGA and VLSI design
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