Biomedical Engineering Reference
In-Depth Information
computing, and Natural computation, with interests in Machine Learning and
belonging to the broader field of Artificial Intelligence. AIS is distinct
from computational immunology and theoretical biology that are concerned with
simulating immunology using computational and mathematical models toward
better understanding the immune system, although such models initiated the field
of AIS and continue to provide a fertile ground for inspiration. Finally, the field of
AIS is not concerned with the investigation of the immune system as a substrate
computation, such as DNA computing. This latter approach has been by far the
most popular while building Information Hiding System.
AIS Technique
The common techniques are inspired by specific immunological theories that
explain the function and behavior of the mammalian adaptive immune system.
• Clonal Selection Algorithm: A class of algorithms inspired by the clonal
selection theory of acquired immunity that explains how B and T lymphocytes
improve their response to antigens over time called affinity maturation. These
algorithms focus on the Darwinian attributes of the theory where selection is
inspired by the affinity of antigen-antibody interactions, reproduction is inspired
by cell division, and variation is inspired by somatic hyper mutation. Clonal
selection algorithms are most commonly applied to optimization and pattern
recognition domains, some of which resemble parallel hill climbing and the
genetic algorithm without the recombination operator.
• Negative Selection Algorithm: Inspired by the positive and negative selection
processes that occur during the maturation of T-cells in the thymus called T-cell
tolerance. Negative selection refers to the identification and deletion (apoptosis)
of self-reacting cells, that is T-cells that may select and attack self-tissues. This
class of algorithms is typically used for classification and pattern recognition
problem domains where the problem space is modeled in the complement of
available knowledge.
• Immune Network Algorithms: Algorithms that describes the regulation of the
immune system by anti-idiotypic antibodies (antibodies that select for other
antibodies). This class of algorithms focus on the network graph structures
involved where antibodies (or antibody producing cells) represent the nodes and
the training algorithm involves growing or pruning edges between the nodes
based on affinity (similarity in the problems representation space). Immune
network algorithms have been used in clustering, data visualization, control, and
optimization domains, and share properties with artificial neural networks.
• Dendritic Cell Algorithms (DCA): The DCA is an example of an immune
inspired algorithm developed using a multiscale approach. This algorithm is
based on an abstract model of dendritic cells (DCs). The DCA is abstracted and
implemented through a process of examining and modeling various aspects of
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