scholarly journals Genetic algorithms and the immune system

2006 ◽  
pp. 319-325 ◽  
Author(s):  
Stephanie Forrest ◽  
Alan S. Perelson
Keyword(s):  
Genetic Algorithms ◽  
Immune System

Using Genetic Algorithms to Explore Pattern Recognition in the Immune System

1993 ◽  
Vol 1 (3) ◽  
pp. 191-211 ◽  
Author(s):  
Stephanie Forrest ◽  
Brenda Javornik ◽  
Robert E. Smith ◽  
Alan S. Perelson
Keyword(s):  
Pattern Recognition ◽  
Genetic Algorithms ◽  
Immune System ◽  
System Model ◽  
Central Component ◽  
Simulation Experiments ◽  
Immune Systems ◽  
Fitness Sharing ◽  
The Individual ◽  
Binary Strings

This paper describes an immune system model based on binary strings. The purpose of the model is to study the pattern-recognition processes and learning that take place at both the individual and species levels in the immune system. The genetic algorithm (GA) is a central component of the model. The paper reports simulation experiments on two pattern-recognition problems that are relevant to natural immune systems. Finally, it reviews the relation between the model and explicit fitness-sharing techniques for genetic algorithms, showing that the immune system model implements a form of implicit fitness sharing.

scholarly journals An Immuno-Genetic Hybrid Algorithm

2009 ◽  
Vol 4 (4) ◽  
pp. 374 ◽  
Author(s):  
Emad Nabil ◽  
Amr Badr ◽  
Ibrahim Farag
Keyword(s):  
Genetic Algorithms ◽  
Immune System ◽  
Hybrid Algorithm ◽  
Clonal Selection ◽  
Fuzzy Rule ◽  
Mutation Rates ◽  
Clonal Selection Algorithm ◽  
Artificial Immune ◽  
Binary Character ◽  
Rule System

The construction of artificial systems by drawing inspiration from natural systems is not a new idea. The Artificial Neural Network (ANN) and Genetic Algorithms (GAs) are good examples of successful applications of the biological metaphor to the solution of computational problems. The study of artificial immune systems is a relatively new field that tries to exploit the mechanisms of the natural immune system (NIS) in order to develop problem- solving techniques. In this research, we have combined the artificial immune system with the genetic algorithms in one hybrid algorithm. We proposed a modification to the clonal selection algorithm, which is inspired from the clonal selection principle and affinity maturation of the human immune responses, by hybridizing it with the crossover operator, which is imported from GAs to increase the exploration of the search space. We also introduced the adaptability of the mutation rates by applying a degrading function so that the mutation rates decrease with time where the affinity of the population increases, the hybrid algorithm used for evolving a fuzzy rule system to solve the wellknown Wisconsin Breast Cancer Diagnosis problem (WBCD). Our evolved system exhibits two important characteristics; first, it attains high classification performance, with the possibility of attributing a confidence measure to the output diagnosis; second, the system has a simple fuzzy rule system; therefore, it is human interpretable. The hybrid algorithm overcomes both the GAs and the AIS, so that it reached the classification ratio 97.36, by only one rule, in the earlier generations than the two other algorithms. The learning and memory acquisition of our algorithm was verified through its application to a binary character recognition problem. The hybrid algorithm overcomes also GAs and AIS and reached the convergence point before them.

An optimization model for product returns using genetic algorithms and artificial immune system

2013 ◽  
Vol 74 ◽  
pp. 156-169 ◽  
Author(s):  
Ali Diabat ◽  
Devika Kannan ◽  
Mathiyazhagan Kaliyan ◽  
Davor Svetinovic
Keyword(s):  
Genetic Algorithms ◽  
Immune System ◽  
Optimization Model ◽  
Artificial Immune System ◽  
Artificial Immune ◽  
Product Returns

scholarly journals Genetic Algorithms and Artificial Life

1994 ◽  
Vol 1 (3) ◽  
pp. 267-289 ◽  
Author(s):  
Melanie Mitchell ◽  
Stephanie Forrest
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Immune System ◽  
Artificial Life ◽  
Computational Models ◽  
Social Systems ◽  
Cognitive Systems ◽  
Future Directions ◽  
Model Ecosystems ◽  
Open Questions

Genetic algorithms are computational models of evolution that play a central role in many artificial-life models. We review the history and current scope of research on genetic algorithms in artificial life, giving illustrative examples in which the genetic algorithm is used to study how learning and evolution interact, and to model ecosystems, immune system, cognitive systems, and social systems. We also outline a number of open questions and future directions for genetic algorithms in artificial-life research.

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