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In this work, an allele gene adaptive mutation (AGAM) and a schemata crossover (SC) operators are designed to improve the performance of the conventional genetic algorithm (GA) and applied to the code design. Specifically, the proposed AGAM exploits both global and local information of the population to maintain an appropriate level of diversity throughout the search process. The SC utilizes high-performance schemata to enhance the traditional crossover operation. The combination of the AGAM and SC achieves both goals of maintaining population diversity and simultaneously sustaining the convergence capability of the GA. As a result, the SC and AGAM based genetic algorithm alleviates the inherent premature convergence of the GA, thus significantly increasing the chance of locating the global optimal solution. The application of the proposed allele based adaptive genetic algorithm (AGAGA) to the universal mobile telecommunications system (UMTS) time-division duplex (TDD) mode leads to significant performance gains on autocorrelation (6.1 dB) and cross-correlation properties (2.0 dB) over those of the current standard.
Date of Publication: May 2011