Optimizing Interleaver for Turbo Codes by Genetic Algorithms.
ABSTRACT Since the appearance in 1993, first approaching the Shannon limit, the Turbo Codes give a new direction for the channel encoding field, especially since they were adopted for multiple norms of telecommunications, such as deeper communication. To obtain an excellent performance it is necessary to design robust turbo code interleaver. We are investigating genetic algorithms as a promising optimization method to find good performing interleaver for the large frame sizes. In this paper, we present our work, compare with several previous approaches and present experimental results.
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ABSTRACT: Genetic Algorithms and Evolution Strategies represent two of the three major Evolutionary Algorithms. This paper examines the history, theory and mathematical background, applications, and the current direction of bo th Genetic Algorithms and Evolution Strategies. Evolutionary Algorithms can be divided into three main areas of research: Genetic Algorithms (GA) (from which both Genetic Programming (which some researchers argue is a fourth main area) and Learning Classifier Systems are based), Evolution Strategies (ES) and Evolutionary Programming. Genetic Programming began as a general model for adaptive process but has since become effective at optimization while Evolution Strategies was designed from the beginning for variable optimization. In section II, the History of both Genetic Algorithms and Evolution Strategies will be examined including areas of research that apply both GA and ES. In section III the theory and mathematical background of GA and ES will be laid out. Additionally, both algorithms will be demonstrated in two separate examples. Finally in section IV a survey of current applications in which GA and ES have been applied is presented.