A Review of Genetic Programming: Popular Techniques, Fundamental Aspects, Software Tools and Applications

Abstract

Genetic Programming (GP) is one of the evolutionary computation (EC) methods followed with great interest by many researchers. When GP first appeared, it has become a popular computational intelligence method because of its successful applications and its potentials to find effective solutions for difficult practical problems of many different disciplines. With the use of GP in a wide variety of areas, numerous variants of GP methods have emerged to provide more effective solutions for computation problems of diverse application fields. Therefore, GP has a very rich literature that is progressively growing. Many GP software tools developed along with process of GP algorithms. There is a need for an inclusive survey of GP literature from the beginning to today of GP in order to reveal the role of GP in the computational intelligence field. This survey study aims to provide an overview of the growing GP literature in a systematic way. The researchers, who need to implement GP methods, can gain insight of potentials in GP methods, their essential drawbacks and prevalent superiorities. Accordingly, taxonomy of GP methods is given by a systematic review of popular GP methods. In this manner, GP methods are analyzed according to two main categories, which consider the discrepancies in their program (chromosome) representation styles and their methodologies. Besides, GP applications in diverse problems are summarized. This literature survey is especially useful for new researchers to gain the required broad perspective before implementing a GP method in their problems.

Comparative and Evaluation of Anomaly Recognition by Employing Statistic Techniques on Humanoid Robot

“…In standard function, robot vision is utilized for part identification and navigation. In advanced function, the vision enables to lead and guide the robots or other automated machines to recognize, think and perform the task, resembling a human [11,12]. By controlling the special machine vision software, it can make the part identification and replace or complement manual inspections, navigation and measurements with digital cameras and imaging processes.…”

DEHypGpOls: a genetic programming with evolutionary hyperparameter optimization and its application for stock market trend prediction

“…Garcia-et al used decision trees(DT) that were generated by GP in order to detect primary financial incident changes (Garcia-Almanza and Tsang 2006). In recent work, Ari et al discussed use of an ensemble average GP model to represent stock exchange market trend based on other international market data (Ari and Alagoz 2021).…”

“…In the current work, authors have preferred an evolutionary hyper-parameter optimization technique to increase the accuracy of GP based stock market trend prediction models. In the previous work, authors demonstrated extracting a regression model of ISE100 index stock market by using ensemble average GP modeling method (Ari and Alagoz 2021). However, this approach cannot yield effective modeling for solutions of the day-ahead forecast problem.…”