A differential evolutionary chromosomal gene expression programming technique for electronic nose applications

The intelligent system applications require automated data-driven modeling tools. The performance consistency of modeling tools is very essential to reduce the need for human intervention. Classical Gene Expression Programmings (GEPs) employ predefined genetic rules for the node-based evolution of expression trees in the absence of optimal numerical values of constant terminals, and these shortcomings can limit the search efficiency of expression trees. To alleviate negative impacts of these limitations on the data-driven GEP modeling performance, a Differential Evolutionary Chromosomal GEP (DEC-GEP) algorithm is suggested. The DEC-GEP utilizes the Differential Evolution (DE) algorithm for the optimization of a complete genotype of expression trees. For this purpose, a modifier gene container, which stores numerical values of constant terminals, is appended to the frame of GEP chromosome, and this modified chromosome structure enables simultaneous optimization of expression tree genotypes together with numerical values of constant terminals. Besides, the DEC-GEP algorithm can benefit from exploration and exploitation capabilities of the DE algorithm for more efficient evolution of GEP expression trees. To investigate consistency of the DEC-GEP algorithm in a data-driven modeling application, an experimental study was conducted for soft calibration of the low-cost, solid-state sensor array measurements, and results indicated that the DEC-GEP could yield dependable CO concentration estimation models for electronic nose applications.