Quantum-evolutionary algorithms: a SW-HW approach

Authors:
D. Porto;A. Martinez;S. Scimone;E. Sciagura
Affiliations:
AST Automotive, STMicroelectronics S.r.l., Catania, Italy;AST Automotive, STMicroelectronics S.r.l., Catania, Italy;AST Automotive, STMicroelectronics S.r.l., Catania, Italy;AST Automotive, STMicroelectronics S.r.l., Catania, Italy
Venue:
CIMMACS'06 Proceedings of the 5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics
Year:
2006

Citing 3
Cited 0

Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization

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Quantum-inspired evolutionary algorithms with a new termination criterion, Hε gate, and two-phase scheme

IEEE Transactions on Evolutionary Computation

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Abstract

In this paper a HW/SW platform for the implementation of an optimizer based on a evolutionary algorithm, called quantum-inspired evolutionary algorithm (QEA), is introduced. It is based on the concept and principles of quantum computing, such as quantum bit and superposition of states, whose features are briefly described. The hardware implementation of the QEA using a FPGA board is therefore described together with the use of a customable software fitness in order to solve general purpose problems. HW-SW connections are provided by a PCI interface. Final aim is to build a flexible object able to optimize the choices of some sensible parameters in a dynamic system quickly, with particular attention on industrial and automotive applications.