GSR: A New Genetic Algorithm for Improving Source and Channel Estimates

Abstract

In this paper, we introduce a new genetic algorithm, which allows us to refine the estimates of information source symbols and channel estimates obtained by any identification algorithm. Instead of searching the entire space, the proposed algorithm searches for the refined estimates in the subspaces near the initial estimate. Creation of initial guesses by using problem specific information and new specially tailored nonblind genetic operators, based on the ideas from schema theory, for realizing the proposed approach are described. The new genetic source symbol refinement (GSR) algorithm is tested to cope with rapidly varying finite-impulse response channels with additive noise model. The method is capable of offering fast convergence with directed search ability and exhibits a unique feature of automatic adjustment in the number of cost function evaluations with the varying signal-to-noise ratio (SNR). Computational results show that the GSR can achieve the bit-error-rate performance near to the simulated annealing bound. As compared with recent sophisticated alternatives for the problem, the GSR performance is superior over a wide range of SNR, with reduced complexity