Evolutionary Multiobjective Optimization on a Chip

The majority of research in evolvable hardware is focused on evolving logic for deployment on reconfigurable hardware. There are far fewer reports concerned with the implementation of evolutionary algorithms (EAs) in hardware. The focus of our research is directed toward using reconfigurable hardware as a means to speed up evolutionary search, and in particular evolutionary multiobjective optimization (EMO). Evolutionary multiobjective optimization utilizes an evolutionary search to find solutions to difficult multiobjective optimization problems. We present an implementation of an EMO algorithm in reconfigurable hardware, and discuss how it may be utilized in practical deployment situations

[1]  Kyrre Glette,et al.  A Flexible On-Chip Evolution System Implemented on a Xilinx Virtex-II Pro Device , 2005, ICES.

[2]  Stephen Marshall,et al.  FPGA realisation of the genetic algorithm for the design of grey-scale soft morphological filters , 2003 .

[3]  A. Wu,et al.  FPGA implementation of four-step genetic search algorithm , 1999, ICECS'99. Proceedings of ICECS '99. 6th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.99EX357).

[4]  David Corne,et al.  The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimisation , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[5]  Ashok Samal,et al.  A Hardware Engine for Genetic Algorithms , 1997 .

[6]  John C. Gallagher,et al.  On the relative efficacies of *cGA variants for intrinsic evolvable hardware; population, mutation, and random immigrants , 2004, Proceedings. 2004 NASA/DoD Conference on Evolvable Hardware, 2004..

[7]  Neal R. Harvey,et al.  Everything on the Chip: A Hardware-Based Self-Contained Spatially-Structured Genetic Algorithm for Signal Processing , 2000, ICES.

[8]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[9]  Arthur C. Sanderson,et al.  Network-based distributed planning using coevolutionary agents: architecture and evaluation , 2004, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[10]  David E. Goldberg,et al.  The compact genetic algorithm , 1999, IEEE Trans. Evol. Comput..

[11]  Peter Martin,et al.  An Analysis Of Random Number Generators For A Hardware Implementation Of Genetic Programming Using FPGAs And Handel-C , 2002, GECCO.

[12]  Arthur C. Sanderson,et al.  Modeling and convergence analysis of distributed coevolutionary algorithms , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[13]  Gunnar Tufte,et al.  Prototyping a GA Pipeline for complete hardware evolution , 1999, Proceedings of the First NASA/DoD Workshop on Evolvable Hardware.

[14]  David J. Evans,et al.  Medical image reconstruction using a multi-objective genetic local search algorithm , 2000, Int. J. Comput. Math..

[15]  Feng Xue,et al.  Management of Complex Dynamic Systems based on Model-Predictive Multi-objective Optimization , 2006, 2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications.

[16]  Adrian Thompson,et al.  Hardware evolution - automatic design of electronic circuits in reconfigurable hardware by artificial evolution , 1999, CPHC/BCS distinguished dissertations.

[17]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[18]  Peter Martin,et al.  A Hardware Implementation of a Genetic Programming System Using FPGAs and Handel-C , 2001, Genetic Programming and Evolvable Machines.