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A Novel Formulation for Estimating Compressive Strength of High Performance Concrete Using Gene Expression Programming
Published in M.Z. Naser, Leveraging Artificial Intelligence in Engineering, Management, and Safety of Infrastructure, 2023
Iman Mansouri, Jale Tezcan, Paul O. Awoyera
Gene expression programming (GEP) is a more recent GA variant, where each member of the population is a chromosome encoded as a linear, symbolic string of fixed length. Despite their fixed length, GEP chromosomes can encode expression trees of different shapes and sizes. According to Ferreira (2006), GA and GP both suffer from the tradeoff between functional complexity and ease of genetic modification, while GEP does not, due to the genotype/phenotype separation, as described in the next section. Researchers from various disciplines have utilized the GEP techniques to solve civil engineering problems (Gandomi et al., 2021; Iqbal et al., 2021; Tadayon et al., 2021; Yao et al., 2021; Zou et al., 2021).
Forecasting the seepage loss for lined and un-lined canals using artificial neural network and gene expression programming
Published in Geomatics, Natural Hazards and Risk, 2023
Manal Gad, Hanaa Mohamed Abdelhaleem, Waleed O. A. S.
An evolutionary method called Gene Expression Programming (GEP) generates computer programs on demand. They can be traditional mathematical models, neural networks, sophisticated nonlinear regression models, logistic regression models, complicated polynomial structures, logic circuits and expressions, and other types of computer programs. However, all GEP programs—regardless of how complicated they are—are encoded in extremely basic linear structures called chromosomes. These straightforward linear chromosomes are a breakthrough since they consistently and correctly encode functional computer programs. We can therefore modify them, pick the best ones to produce, develop stronger programs from them, and so on indefinitely. Of course, this is one of the requirements for having an effective system that is constantly seeking for new and better solutions.
Selected AI optimization techniques and applications in geotechnical engineering
Published in Cogent Engineering, 2023
Kennedy C. Onyelowe, Farid F. Mojtahedi, Ahmed M. Ebid, Amirhossein Rezaei, Kolawole J. Osinubi, Adrian O. Eberemu, Bunyamin Salahudeen, Emmanuel W. Gadzama, Danial Rezazadeh, Hashem Jahangir, Paul Yohanna, Michael E. Onyia, Fazal E. Jalal, Mudassir Iqbal, Chidozie Ikpa, Ifeyinwa I. Obianyo, Zia Ur Rehman
The technique known as Gene Expression Programming (GEP) makes use of population in this case population of models and solutions, selects and reproduces them according to fitness, and introduces genetic variation using one or more genetic operators such as mutation or recombination (Mitchell, 1998). Though the GEP can be likened to the GA and GP as the two still operates on the principle of population, the fundamental distinction among the three algorithms is dependent on the nature of the individuals or models or solutions as the case may be; in GA the individuals are symbolic strings of fixed chromosomes, in GP the individuals are non-linear entities of different sizes and shapes while in GEP the individuals are encoded in symbolic strings of fixed chromosomes which are expressed as GPs, this means that GEP is a combination of GA and GP.
Mathematical expression for discharge coefficient of Weir-Gate using soft computing techniques
Published in Journal of Applied Water Engineering and Research, 2021
Abbas Parsaie, Amir Hamzeh Haghiabi
Gene expression programming (GEP) is a soft computing technique that automatically creates mathematical phrases such conventional mathematical models, neural networks, decision trees, sophisticated nonlinear regression models, logistic regression models, nonlinear classifiers, complex polynomial structures, logic circuits and expressions, and so on (Zeynoddin et al. 2020). But irrespective of their complexity, all GEP programs are encoded in very simple linear structures. These simple linear chromosomes are a breakthrough because, no matter what, they always encode valid computer programs. So we can mutate them and then select the best ones to reproduce and then create better programs and so on, endlessly. This is, of course, one of the prerequisites for having a system evolving efficiently, searching for better and better solutions for all kinds of problems (Ferreira 2006; Parsaie et al. 2020).