Parallel dedicated hardware devices for heterogeneous computations
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
A memetic algorithm for the low autocorrelation binary sequence problem
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Finding low autocorrelation binary sequences with memetic algorithms
Applied Soft Computing
Autocorrelation properties of OFDM timing synchronization waveforms employing pilot subcarriers
EURASIP Journal on Wireless Communications and Networking - Special issue on synchronization in wireless communications
GRASP for low autocorrelated binary sequences
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
A survey of the merit factor problem for binary sequences
SETA'04 Proceedings of the Third international conference on Sequences and Their Applications
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The search for low autocorrelated binary sequences is a classical example of a discrete frustrated optimization problem. We demonstrate the efficiency of a class of evolutionary algorithms to tackle the problem. A suitable mutation operator using a preselection scheme is constructed, and the optimal parameters of the strategy are determined