Quantum-Inspired Multi-gene Linear Genetic Programming Model for Regression Problems

摘要

We propose the Quantum-Inspired Multi-Gene Lin-ear Genetic Programming (QIMuLGP), which is a generalization of Quantum-Inspired Linear Genetic Programming (QILGP) model for symbolic regression. QIMuLGP allows us to explore a different genotypic representation (i.e. linear), and to use more than one genotype per individual, combining their outputs using least squares method (multi-gene approach). We used 11 benchmark problems to experimentally compare QIMuLGP with: canonical tree Genetic Programming, Multi-Gene tree-based GP (MGGP), and QILGP. QIMuLGP obtained better results than QILGP in almost all experiments performed. When compared to MGGP, QIMuLGP achieved equivalent errors for some experiments with its runtime always shorter (up to 20 times and 8 times on average), which is an important advantage in high dimensional-scalable problems.