Stack computers: the new wave
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Communications of the ACM
Inductive genetic programming with immune network dynamics
Advances in genetic programming
Artificial Intelligence: Structures and Strategies for Complex Problem Solving
Artificial Intelligence: Structures and Strategies for Complex Problem Solving
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
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Using genetic algorithms to explore pattern recognition in the immune system
Evolutionary Computation
High-performance, parallel, stack-based genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Design of mixed H2/H∞ control systems using algorithms inspired by the immune system
Information Sciences: an International Journal
An IP and GEP Based Dynamic Decision Model for Stock Market Forecasting
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
Rule extraction from trained adaptive neural networks using artificial immune systems
Expert Systems with Applications: An International Journal
Clone selection programming and its application to symbolic regression
Expert Systems with Applications: An International Journal
Dynamic population variation in genetic programming
Information Sciences: an International Journal
ACS'08 Proceedings of the 8th conference on Applied computer scince
Immune programming models of Cryptosporidium parvum inactivation by ozone and chlorine dioxide
Information Sciences: an International Journal
Extracting rules for classification problems: AIS based approach
Expert Systems with Applications: An International Journal
Grammar-Based Immune Programming for Symbolic Regression
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
Learning to rank using evolutionary computation: immune programming or genetic programming?
Proceedings of the 18th ACM conference on Information and knowledge management
Artificial immune systems for assembly sequence planning exploration
Engineering Applications of Artificial Intelligence
Watermarking schema using an artificial immune system in spatial domain
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Near-optimal solution to pair-wise LSB matching via an immune programming strategy
Information Sciences: an International Journal
An immune programming-based ranking function discovery approach for effective information retrieval
Expert Systems with Applications: An International Journal
Feature generation in fault diagnosis based on immune programming
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
Directly optimizing evaluation measures in learning to rank based on the clonal selection algorithm
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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
A parallel evolving algorithm for flexible neural tree
Parallel Computing
Structural design of the danger model immune algorithm
Information Sciences: an International Journal
Damage detection based on improved particle swarm optimization using vibration data
Applied Soft Computing
Artificial bee colony programming for symbolic regression
Information Sciences: an International Journal
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This paper describes 'Immune Programming', a paradigm in the field of evolutionary computing taking its inspiration from principles of the vertebrate immune system. These principles are used to derive stack-based computer programs to solve a wide range of problems. An antigen is used to represent the programming problem to be addressed and may be provided in closed form or as an input/output mapping. An antibody set (a repertoire), wherein each member represents a candidate solution, is generated at random from a gene library representing computer instructions. Affinity, the fit of an antibody (a solution candidate) to the antigen (the problem), is analogous to shape-complementarity evident in biological systems. This measure is used to determine both the fate of individual antibodies, and whether or not the algorithm has successfully completed. When a repertoire has not yielded affinity relating algorithm completion, individual antibodies are replaced, cloned, or hypermutated. Replacement occurs according to a replacement probability and yields an entirely new randomly-generated solution candidate when invoked. This randomness (and that of the initial repertoire) provides diversity sufficient to address a wide range of problems. The chance of antibody cloning, wherein a verbatim copy is placed in the new repertoire, occurs proportionally to its affinity and according to a cloning probability. The chances of an effective (high-affinity) antibody being cloned is high, analogous to replication of effective pathogen-fighting antibodies in biological systems. Hypermutation, wherein probability-based replacement of the gene components within an antibody occurs, is also performed on high-affinity entities. However, the extent of mutation is inversely proportional to the antigenic affinity. The effectiveness of this process lies in the supposition that a candidate showing promise is likely similar to the ideal solution. This paper describes the paradigm in detail along with the underlying immune theories and their computational models. A set of sample problems are defined and solved using the algorithm, demonstrating its effectiveness and excellent convergent qualities. Further, the speed of convergence with respect to repertoire size limitations and probability parameters is explored and compared to stack-based genetic programming algorithms.