Using Differential Evolution for GEP Constant Creation
نویسندگان
چکیده
Gene Expression Programming (GEP) is a new evolutionary algorithm that incorporates both the idea of simple, linear chromosome of fixed length used in Genetic Algorithms (GAs) and the ramified structure of different sizes and shapes used in Genetic Programming (GP). Same as other genetic programming algorithms, GEP has difficulty finding appropriate numeric constants for terminal nodes in the expression trees. In this work, we describe a new approach of constants generation using Differential Evolution (DE), which is a simple real-valued GA that has been proved robust and efficient on parameter optimization problems. Our experimental results on two symbolic regression problems show that the approach significantly improves the performance of the GEP algorithm. The proposed approach can be easily extended to other Genetic Programming variants.
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