Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
An Immunological Approach to Combinatorial Optimization Problems
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm
Genetic Programming and Evolvable Machines
The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics)
Proceedings of the 2006 ACM symposium on Applied computing
New EM derived from Kullback-Leibler divergence
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Using genetic algorithms to explore pattern recognition in the immune system
Evolutionary Computation
Theoretical advances in artificial immune systems
Theoretical Computer Science
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Adaptable Lymphocytes for Artificial Immune Systems
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
On AIRS and Clonal Selection for Machine Learning
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
Online EM for unsupervised models
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
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Clonal selection has been a dominant theme in many immune-inspired algorithms applied to machine learning and optimisation. We examine existing clonal selection algorithms for learning from a theoretical and empirical perspective and assert that the widely accepted computational interpretation of clonal selection is compromised both algorithmically and biologically. We suggest a more capable abstraction of the clonal selection principle grounded in probabilistic estimation and approximation and demonstrate how it addresses some of the shortcomings in existing algorithms. We further show that by recasting black-box optimisation as a learning problem, the same abstraction may be re-employed; thereby taking steps toward unifying the clonal selection principle and distinguishing it from natural selection.