Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Review: Discipulus: A Commercial Genetic Programming System
Genetic Programming and Evolvable Machines
A comparison of linear genetic programming and neural networks inmedical data mining
IEEE Transactions on Evolutionary Computation
Modeling of the angle of shearing resistance of soils using soft computing systems
Expert Systems with Applications: An International Journal
Soil liquefaction modeling by Genetic Expression Programming and Neuro-Fuzzy
Expert Systems with Applications: An International Journal
Permanent deformation analysis of asphalt mixtures using soft computing techniques
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Using weighted genetic programming to program squat wall strengths and tune associated formulas
Engineering Applications of Artificial Intelligence
Genetic-based modeling of uplift capacity of suction caissons
Expert Systems with Applications: An International Journal
A new predictive model for compressive strength of HPC using gene expression programming
Advances in Engineering Software
Predicting axial capacity of driven piles in cohesive soils using intelligent computing
Engineering Applications of Artificial Intelligence
Advances in Artificial Neural Systems
Application of genetic programming for modelling of material characteristics
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Improving analytical models of circular concrete columns with genetic programming polynomials
Genetic Programming and Evolvable Machines
An improved multi-expression programming algorithm applied in function discovery and data prediction
International Journal of Information and Communication Technology
Hi-index | 12.06 |
Accurate determination of compressive and tensile strength of limestone is an important subject for the design of geotechnical structures. Although there are several classical approaches in the literature for strength prediction their predictive accuracy is generally not satisfactory. The trend in the literature is to apply artificial intelligence based soft computing techniques for complex prediction problems. Artificial neural networks which are a member of soft computing techniques were applied to strength prediction of several types of rocks in the literature with considerable success. Although artificial neural networks are successful in prediction, their inability to explicitly produce prediction equations can create difficulty in practical circumstances. Another member of soft computing family which is known as genetic programming can be a very useful candidate to overcome this problem. Genetic programming based approaches are not yet applied to the strength prediction of limestone. This paper makes an attempt to apply a promising set of genetic programming techniques which are known as multi expression programming (MEP), gene expression programming (GEP) and linear genetic programming (LGP) to the uniaxial compressive strength (UCS) and tensile strength prediction of chalky and clayey soft limestone. The data for strength prediction were generated experimentally in the University of Gaziantep civil engineering laboratories by using limestone samples collected from Gaziantep region of Turkey.