Evolving recurrent models using linear GP

Turing complete Genetic Programming (GP) models introduce the concept of internal state, and therefore have the capacity for identifying interesting temporal properties. Surprisingly, there is little evidence of the application of such models to problems for prediction. An empirical evaluation is made of a simple recurrent linear GP model over standard prediction problems. Categories and Subject Descriptors I.2.6 [Computing Methodologies]: Learning – Parameter Learning. General Terms Algorithms, Experimentation, Languages. Keywords Recurrent Architectures, Linear Genetic Programming.

• Type: Conference
• Year: 2005
• Where: GECCO
• Authors: Xiao Luo, Malcolm I. Heywood, A. Nur Zincir-Heywood
• Keywords: Complete Genetic Programming, GECCO 2005, Genetic Programming, Recurrent Linear Gp