A New Algorithm for Detection of Animal and Plant Ion Concentration Based on Gene Expression Programming

A New Algorithm for Detection of Animal and Plant Ion Concentration Based on Gene Expression Programming

Kangshun Li, Leqing Lin, Jiaming Li, Siwei Chen, Hassan Jalil
DOI: 10.4018/IJCINI.318144
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Abstract

In order to accurately predict the concentration detection data of ion sensors for animal and plant, this paper proposes a gene expression programming (GEP) based concentration detection method. The method includes collecting ion concentration data as well as voltage timing data; preprocessing all the collected data to obtain an initial sample set; constructing a prediction model of ion concentration, which is an explicit functional relationship between voltage and the concentration of a specific ion. The Gene Expression Programming is used to train and evaluate the prediction model, and obtain a trained model. By comparing gene expression programming with other two modeling methods, it is found that the accuracy of the model established by gene expression programming has greater advantages than that established by polynomial fitting and neural network in processing animal and plant ion concentration data.
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Gene Expression Programming

Gene expression programming was created by Portuguese scientist Ferreira. It is a development of GA (genetic algorithm) and GP (genetic programming design). GEP combines the advantages of both, including the simple and fixed-length coding characteristics of GA, and the indefinite length and indefinite characteristics of the tree structure in GP. Therefore, gene expression programming is much faster than GA or GP.

Gene expression programming processes chromosomes, which consist of genes connected by linking functions. A gene consists of a head and a tail, and the head contains function sets and terminals, while the tail contains only terminals. Thereinto:

IJCINI.318144.m01
(1) where t represents the length of the gene tail, h represents the length of the gene head, and n denotes the maximum number of parameters in the function set.

K-Expressions

Chromosomes are made up of one or more fixed-length, linear, equal-length genes, so genes are also linear and fixed-length. Chromosomes can determine the size and shape of the expression tree. For example, in this simple algebraic equation:

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(2)

The expression tree of this equation is shown in Figure 1, where q represents the square root. Traversing the expression tree in Figure 1 from top to bottom and left to right yields the corresponding K-expression, as shown in equation (3). The genotype in gene expression programming is:

IJCINI.318144.m03
IJCINI.318144.m04
(3)
Figure 1.

The Expression Tree for Equation (2)

IJCINI.318144.f01

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