Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Generalized Convergence Models for Tournament- and (mu, lambda)-Selection
Proceedings of the 6th International Conference on Genetic Algorithms
A Coevolutionary Approach to Learning Sequential Decision Rules
Proceedings of the 6th International Conference on Genetic Algorithms
Blind estimation of direct sequence spread spectrum signals inmultipath
IEEE Transactions on Signal Processing
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In the context of spectrum surveillance, a new method to recover the code of spread spectrum signal is presented, while the receiver has no knowledge of the transmitter's spreading sequence. As previous work, a conventional Genetic algorithm (GA) was used to recover spreading code. Although genetic algorithms (GAs) are well known for their robustness in solving complex optimization problems, but nonetheless, by increasing the length of the code, we will often lead to an unacceptable slow convergence speed. To solve this problem we introduce Time Variant Genetic Algorithm (TV-GA) into code estimation in spread spectrum communication system. In searching process for code estimation, the TV-GA algorithm has the merits of rapid convergence to the global optimum, without being trapped in local suboptimum, and good robustness to noise. In this paper we describe how to implement TV-GA as a component of a searching algorithm in code estimation. TV-GA boasts a number of advantages due to the use of mobile agents. Some of them are: Scalability, Fault tolerance, Adaptation, Speed, Modularity, Autonomy, and Parallelism. These properties make TV-GA very attractive for spread spectrum code estimation. They also make TV-GA suitable for a variety of other kinds of channels. Our results compare between Time Variant Genetic algorithm (TVGA) and conventional Genetic algorithms (GA), and also show time variant Genetic algorithm performance in code estimation process.