Human Motion Model Construction Based on Gene Expression Programming

He, Wei; Hu, Shaoyang; Li, Shanni; Jin, Junlin; Li, Kangshun

doi:10.1007/978-981-13-1651-7_42

Cited by 1 publication

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Computer vision: GP algorithms have been utilized in computer vision applications e.g. recognition of human motion [18], improving the performance of the histogram of oriented gradients (HOG) algorithm for human detection problems [76], in robotics [77], human movement modeling [78], improving edge detection in images [79] and in the pattern recognition problems [80] etc. Circuit Design and Evolvable hardware: Evolutionary Hardware (EHW) is a design approach that uses a reconfigurable hardware structure to develop a circuit that performs a specific function.…”

Section: Artificial Neural Network (Ann) Design: a Corporation Of Artificial Neural Network (Ann)mentioning

confidence: 99%

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

Ari

Alagöz

2021

Sakarya University Journal of Science

View full text Add to dashboard Cite

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.

show abstract