Prediction of compressive and tensile strength of limestone via genetic programming
Expert Systems with Applications: An International Journal
Damage detection of truss bridge joints using Artificial Neural Networks
Expert Systems with Applications: An International Journal
Knowledge discovery of concrete material using Genetic Operation Trees
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Generalization performance of support vector machines and neural networks in runoff modeling
Expert Systems with Applications: An International Journal
Hybrid high order neural networks
Applied Soft Computing
Expert Systems with Applications: An International Journal
Genetic programming for predicting aseismic abilities of school buildings
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
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This study developed a weighted genetic programming (WGP) approach to study the squat wall strength. The proposed WGP evolves on genetic programming (GP), an evolutionary algorithm-based methodology that employs a binary tree topology and optimized functional operators. Weight coefficients were introduced to each GP linkage in the tree in order to create a new weighted genetic programming (WGP) approach. The proposed WGP offers two distinct advantages, including: (1) a balance of influences is struck between the two front input branches and (2) weights are incorporated throughout generated formulas. Resulting formulas contain a certain quantity of optimized functions and weights. Genetic algorithms are employed to accomplish WGP optimization of function selection and proper weighting tasks. Case studies herein focused on a reference study of squat wall strength. Results demonstrated that the proposed WGP provides accurate results and formula outputs. This paper further utilized WGP to tune referenced formulas, which yielded a final formula that combined the positive attributes of both WGP and analytical models.