Abstract
The Binary classification is the most challenging problem in machine learning. One of the most promising technique to solve this problem is by implementing genetic programming (GP). GP is one of Evolutionary Algorithm (EA) that used to solve problems that humans do not know how to solve it directly. The objectives of this research is to demonstrate the use of genetic programming in this type of problems; that is, other types of techniques are typically used, e.g., regression, artificial neural networks. Genetic programming presents an advantage compared to those techniques, which is that it does not need an a priori definition of its structure. The algorithm evolves automatically until finding a model that best fits a set of training data. Feature engineering was considered to improve the accuracy. In this research, feature transformation and feature creation were implemented. Thus, genetic programming can be considered as an alternative option for the development of intelligent systems mainly in the pattern recognition field.
Highlights
Today it is more common to find computer programs that implement artificial intelligence in their operations
The crossing is applied in each generation to create new trees from existing ones, which would replace the worst individuals in the population
Each pair of parents is selected, a random node is chosen from each parent for the cut, and subsequently the sub-exchange is made tree or genetic material
Summary
Today it is more common to find computer programs that implement artificial intelligence in their operations. It could generate as an application that aim to facilitate the daily life of the human being through the use of ICTs (Information and Communications Technology) as facilitating tools [6], [20], ie, smartphones, security systems, etc. According to [19], it can be said that virtualization, "is an abstraction of technological resources where you can get to use a server or many servers being invisible to the end user." One of these operations is the recognition of patterns, such as the voice. The recognition of patterns has allowed the development of automated systems that are increasingly used and demanded. Within the stages of pattern recognition, classification is a primordial phase, which can be defined as the assignment of a value to something according to its qualities or characteristics. For the development of models to predict whether a business has financial risk, that is, whether it is feasible to invest in the business or not [9]
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