Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
The evolution of evolvability in genetic programming
Advances in genetic programming
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Approximating geometric crossover in semantic space
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Semantically driven mutation in genetic programming
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Bankruptcy prediction with neural logic networks by means of grammar-guided genetic programming
Expert Systems with Applications: An International Journal
Human-competitive results produced by genetic programming
Genetic Programming and Evolvable Machines
Promoting phenotypic diversity in genetic programming
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Soil liquefaction modeling by Genetic Expression Programming and Neuro-Fuzzy
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Application of genetic programming for modelling of material characteristics
Expert Systems with Applications: An International Journal
Geometric semantic genetic programming
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
A new implementation of geometric semantic GP and its application to problems in pharmacokinetics
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
Performance evaluation of microbial fuel cell by artificial intelligence methods
Expert Systems with Applications: An International Journal
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Concrete is a composite construction material made primarily with aggregate, cement, and water. In addition to the basic ingredients used in conventional concrete, high-performance concrete incorporates supplementary cementitious materials, such as fly ash and blast furnace slag, and chemical admixture, such as superplasticizer. Hence, high-performance concrete is a highly complex material and modeling its behavior represents a difficult task. In this paper, we propose an intelligent system based on Genetic Programming for the prediction of high-performance concrete strength. The system we propose is called Geometric Semantic Genetic Programming, and it is based on recently defined geometric semantic genetic operators for Genetic Programming. Experimental results show the suitability of the proposed system for the prediction of concrete strength. In particular, the new method provides significantly better results than the ones produced by standard Genetic Programming and other machine learning methods, both on training and on out-of-sample data.