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Limitations of GEP decoding Analysis and Countermeasures

Author: WangXiao
Tutor: HeZuo
School: Changsha University of Science and Technology
Course: Computer Software and Theory
Keywords: Genetic Algorithms Genetic Programming Gene Expression Programming GEP decoding
CLC: TP18
Type: Master's thesis
Year: 2011
Downloads: 34
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Abstract


Gene Expression Programming (Gene Expression Programming, GEP) is a member of a new generation of evolutionary computation , which combines the genetic algorithm (Genetic Algorithm, GA) coding is simple, easy to operate and Genetic Programming (Genetic Programming, GP) expression flexible search capabilities strong features, evolutionary genetic programming system efficiency than higher 100-60000 times , due to its superior performance to attract more and more researchers involved. People GEP individual coding structure , decoding method , population initialization , each genetic operator , etc. has been improved, made ??a very good test results , GEP combined with the traditional methods of artificial intelligence evolution efficiency is also improved. GEP model theoretically theorem, convergence studies have achieved some results. GEP is now widely used in symbolic regression , classification algorithms , time series prediction . After all GEP algorithm proposed up to now , but ten years have elapsed , theoretical and applied research in many areas there are waiting for latecomers to explore. In this paper, on the basis of previous work on GEP Research , theory, decoding method and application were studied. The main work includes the following aspects of research and innovation : a , introduces traditional genetic algorithms and genetic programming related theories and technologies ; 2 , focusing on the gene expression programming algorithm is the key factor and process analysis the GEP with GA and GP essential difference : GEP achieve the separation of genotype and phenotype ; 3 analyzes the shortcomings of traditional GEP decoding , we propose a new decoding method GEP : GEP decoding non-physical tree , this method in the decoding process without real physical meaning of the expression tree on the establishment and operation of the machine to reduce the amount of computation ; 4 , the new decoding algorithm is applied to symbolic regression with GEP stock trend forecast and achieved good experimental results ; 5, the final work on the text of a summary , this paper analyzes the existing problems and the future direction of development were discussed.

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