Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
A compiling genetic programming system that directly manipulates the machine code
Advances in genetic programming
Structural learning with forgetting
Neural Networks
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Information Sciences: an International Journal
Regularization approach to inductive genetic programming
IEEE Transactions on Evolutionary Computation
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
An Immune Algorithm for Protein Structure Prediction on Lattice Models
IEEE Transactions on Evolutionary Computation
Grammar-Based Immune Programming for Symbolic Regression
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
Grammar-based immune programming
Natural Computing: an international journal
Inferring systems of ordinary differential equations via grammar-based immune programming
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
Hi-index | 0.08 |
This paper presents analytical models of Cryptosporidium parvum inactivation that have been evolved using immune programming. The objective of these models is to predict the reduction of infectivity associated with the disinfection by ozone and chlorine dioxide. To solve this problem, we introduce a modified immune programming approach together with corresponding implementation of the immune algorithm. The modeling results indicate that models obtained with immune programming outperform the traditional temperature corrected Chick-Watson models, as well as previously developed artificial neural network models. Detailed analysis of modeling errors, prediction power, and behavior of the models are included. Obtained models reveal that some input attributes have no effect on the prediction performance. This finding corresponds to the results previously obtained by saliency analysis of neural models. Results obtained in this study suggest that immune programming is becoming a mature technology which is ready for wide implementation in applications.