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Advisor(s)
Abstract(s)
Evolutionary-based algorithms play an important role in finding solutions to many problems that are not solved
by classical methods, and particularly so for those cases where solutions lie within extreme non-convex multidimensional
spaces. The intrinsic parallel structure of evolutionary algorithms are amenable to the simultaneous
testing of multiple solutions; this has proved essential to the circumvention of local optima, and such
robustness comes with high computational overhead, though custom digital processor use may reduce this
cost. This paper presents a new implementation of an old, and almost forgotten, evolutionary algorithm: the
population-based incremental learning method. We show that the structure of this algorithm is well suited to
implementation within programmable logic, as compared with contemporary genetic algorithms. Further, the
inherent concurrency of our FPGA implementation facilitates the integration and testing of micro-populations.
Description
Keywords
Population based incremental learning Multi-population evolutionary algorithms FPGA
Citation
Coelho, J.P.; Pinho, T.; Boaventura-Cunha, J. (2015). FPGA implementation of a multi-population PBIL algorithm. In 7th International Joint Conference on Computational Intelligence (IJCCI 2015). Lisboa