Quantum‐inspired genetic programming model with application to predict toxicity degree for chemical compounds

Cheminformatics plays a vital role to maintain a large amount of chemical data. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in domains such as cosmetics, drug design, food safety, and manufacturing chemical compounds. Toxicity prediction topic requires several new approaches for knowledge discovery from data to paradigm composite associations between the modules of the chemical compound; such techniques need more computational cost as the number of chemical compounds increases. State-of-the-art prediction methods such as neural network and multi-layer regression that requires either tuning parameters or complex transformations of predictor or outcome variables are not achieving high accuracy results. This paper proposes a Quantum Inspired Genetic Programming “QIGP” model to improve the prediction accuracy. Genetic Programming is utilized to give a linear equation for calculating toxicity degree more accurately. Quantum computing is employed to improve the selection of the best-of-run individuals and handles parsimony pressure to reduce the complexity of the solutions. The results of the internal validation analysis indicated that the QIGP model has the better goodness of fit statistics and significantly outperforms the Neural Network model.

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Genetic programming model for forecast of short and noisy data

Hydrological Processes ◽

10.1002/hyp.6226 ◽

2007 ◽

Vol 21 (2) ◽

pp. 266-272 ◽

Cited By ~ 23

Author(s):

C. Sivapragasam ◽

P. Vincent ◽

G. Vasudevan

Keyword(s):

Genetic Programming ◽

Programming Model ◽

Noisy Data

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A new genetic programming model for predicting settlement of shallow foundations

Canadian Geotechnical Journal ◽

10.1139/t07-063 ◽

2007 ◽

Vol 44 (12) ◽

pp. 1462-1473 ◽

Cited By ~ 66

Author(s):

Mohammad Rezania ◽

Akbar A. Javadi

Keyword(s):

Genetic Programming ◽

Programming Model ◽

Standard Penetration Test ◽

Shallow Foundations ◽

Penetration Test ◽

Case Histories ◽

Contributing Factors ◽

Artificial Neural Network Ann ◽

Gp Model ◽

Very High

In this paper, a new genetic programming (GP) approach for predicting settlement of shallow foundations is presented. The GP model is developed and verified using a large database of standard penetration test (SPT) based case histories that involve measured settlements of shallow foundations. The results of the developed GP model are compared with those of a number of commonly used traditional methods and artificial neural network (ANN) based models. It is shown that the GP model is able to learn, with a very high accuracy, the complex relationship between foundation settlement and its contributing factors, and render this knowledge in the form of a function. The attained function can be used to generalize the learning and apply it to predict settlement of foundations for new cases not used in the development of the model. The advantages of the proposed GP model over the conventional and ANN based models are highlighted.

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