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Journal ArticleDOI

The immune system, adaptation, and machine learning

01 Oct 1986-Physica D: Nonlinear Phenomena (North-Holland)-Vol. 2, Iss: 1, pp 187-204
TL;DR: A dynamical model for the immune system is described that is based on the network hypothesis of Jerne, and is simple enough to simulate on a computer, and has a strong similarity to an approach to learning and artificial intelligence introduced by Holland, called the classifier system.
About: This article is published in Physica D: Nonlinear Phenomena.The article was published on 1986-10-01. It has received 1326 citations till now. The article focuses on the topics: Artificial immune system & Learning classifier system.
Citations
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Journal ArticleDOI
TL;DR: A new optimization algorithm based on the law of gravity and mass interactions is introduced and the obtained results confirm the high performance of the proposed method in solving various nonlinear functions.

5,501 citations

Book
22 Jun 2009
TL;DR: This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling.
Abstract: A unified view of metaheuristics This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. Throughout the book, the key search components of metaheuristics are considered as a toolbox for: Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems Designing efficient metaheuristics for multi-objective optimization problems Designing hybrid, parallel, and distributed metaheuristics Implementing metaheuristics on sequential and parallel machines Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics.

2,735 citations


Cites background or methods from "The immune system, adaptation, and ..."

  • ...It has powerful learning and memory capabilities and presents an evolutionary type of response to infectious foreign elements [70,253]....

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  • ...The original references of the following metaheuristics are based on their application to optimization and/or machine learning problems: ACO (ant colonies optimization) [215], AIS (artificial immune systems) [70,253], BC (bee colony) [689,835], CA (cultural algorithms) [652], CEA (coevolutionary algorithms) [375,397], CMA-ES (covariance matrix...

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Proceedings ArticleDOI
16 May 1994
TL;DR: A method for change detection which is based on the generation of T cells in the immune system is described, which reveals computational costs of the system and preliminary experiments illustrate how the method might be applied to the problem of computer viruses.
Abstract: The problem of protecting computer systems can be viewed generally as the problem of learning to distinguish self from other. The authors describe a method for change detection which is based on the generation of T cells in the immune system. Mathematical analysis reveals computational costs of the system, and preliminary experiments illustrate how the method might be applied to the problem of computer viruses. >

1,782 citations


Cites background from "The immune system, adaptation, and ..."

  • ...immune-system models based on a universe in which antigens (foreign material) and antibodies (the cells that perform the recognition) are represented by binary strings [2, 11, 4, 3]....

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Journal ArticleDOI
TL;DR: The complexity of ML/DM algorithms is addressed, discussion of challenges for using ML/ DM for cyber security is presented, and some recommendations on when to use a given method are provided.
Abstract: This survey paper describes a focused literature survey of machine learning (ML) and data mining (DM) methods for cyber analytics in support of intrusion detection. Short tutorial descriptions of each ML/DM method are provided. Based on the number of citations or the relevance of an emerging method, papers representing each method were identified, read, and summarized. Because data are so important in ML/DM approaches, some well-known cyber data sets used in ML/DM are described. The complexity of ML/DM algorithms is addressed, discussion of challenges for using ML/DM for cyber security is presented, and some recommendations on when to use a given method are provided.

1,704 citations


Additional excerpts

  • ...Methods such as Artificial Neural Networks (ANNs), Fuzzy Systems, Evolutionary Computation, Artificial Immune Systems, and Swarm Intelligence are described in great detail....

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  • ...The term evolutionary computation encompasses Genetic Algorithms (GA) [67], Genetic Programming (GP) [68], Evolution Strategies [69], Particle Swarm Optimization [70], Ant Colony Optimization [71], and Artificial Immune Systems [72]....

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  • ...Evolution Strategies [69], Particle Swarm Optimization [70], Ant Colony Optimization [71], and Artificial Immune Systems [72]....

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Journal ArticleDOI
TL;DR: An efficient optimization method called 'Teaching-Learning-Based Optimization (TLBO)' is proposed in this paper for large scale non-linear optimization problems for finding the global solutions.

1,359 citations


Additional excerpts

  • ...Some of the well known meta-heuristics developed during the last three decades are: Genetic Algorithm (GA) [12] which works on the principle of the Darwinian theory of the survival of the fittest and the theory of evolution of the living beings; Artificial Immune Algorithm (AIA) [9] which works on the immune system of the human being; Ant Colony Optimization (ACO) [6] which works on the foraging behavior of the ant for the food; Particle Swarm Optimization (PSO) [16] which works on the foraging behavior of the swarm of birds; Differential Evolution (DE) [7,26] which is similar to GA with specialized crossover and selection method; Harmony Search (HS) [10] which works on the principle of music improvisation in a music player; Bacteria Foraging Optimization (BFO) [20] which works on the behavior of bacteria; Shuffled Frog Leaping (SFL) [8] which works on the principle of communication among the frogs, Artificial Bee Colony (ABC) [3,13] which works on the foraging behavior of a honey bee; Biogeography-Based Optimization (BBO) [25] which works on the principle of immigration and emigration of the species from one place to the other; Gravitational Search Algorithm (GSA) [22] which works on the principle of gravitational force acting between the bodies; Grenade Explosion Method (GEM) [1] which works on the...

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  • ...Some of the well known meta-heuristics developed during the last three decades are: Genetic Algorithm (GA) [12] which works on the principle of the Darwinian theory of the survival of the fittest and the theory of evolution of the living beings; Artificial Immune Algorithm (AIA) [9] which works on the immune system of the human being; Ant Colony Optimization (ACO) [6] which works on the foraging behavior of the ant for the food; Particle Swarm Optimization (PSO) [16] which works on the foraging behavior of the swarm of birds; Differential Evolution (DE) [7,26] which is similar to GA with specialized crossover and selection method; Harmony Search (HS) [10] which works on the principle of music improvisation in a music player; Bacteria Foraging Optimization (BFO) [20] which works on the behavior of bacteria; Shuffled Frog Leaping (SFL) [8] which works on the principle of communication among the frogs, Artificial Bee Colony (ABC) [3,13] which works on the foraging behavior of a honey bee; Biogeography-Based Optimization (BBO) [25] which works on the principle of immigration and emigration of the species from one place to the other; Gravitational Search Algorithm (GSA) [22] which works on the principle of gravitational force acting between the bodies; Grenade Explosion Method (GEM) [1] which works on the ....

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References
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Journal ArticleDOI
14 Apr 1983-Nature
TL;DR: In the genome of a germ-line cell, the genetic information for an immunoglobulin polypeptide chain is contained in multiple gene segments scattered along a chromosome which are assembled by recombination which leads to the formation of a complete gene.
Abstract: In the genome of a germ-line cell, the genetic information for an immunoglobulin polypeptide chain is contained in multiple gene segments scattered along a chromosome. During the development of bone marrow-derived lymphocytes, these gene segments are assembled by recombination which leads to the formation of a complete gene. In addition, mutations are somatically introduced at a high rate into the amino-terminal region. Both somatic recombination and mutation contribute greatly to an increase in the diversity of antibody synthesized by a single organism.

3,679 citations

Book ChapterDOI
TL;DR: In this article, the application of statistical analysis and statistical mechanics to the problem of turbulent fluid motion has attracted much attention in recent years, and the authors investigated a complicated system of nonlinear equations, in order to find out enough about the properties of the solutions of these equations that insight can be obtained into the various patterns exhibited by the field and that data can be derived concerning the relative frequencies of these patterns in the hope that in this way a basis may be found for the calculation of important values.
Abstract: Publisher Summary This chapter discusses that the application of methods of statistical analysis and statistical mechanics to the problem of turbulent fluid motion has attracted much attention in recent years. It investigated a complicated system of nonlinear equations, in order to find out enough about the properties of the solutions of these equations that insight can be obtained into the various patterns exhibited by the field and that data can be derived concerning the relative frequencies of these patterns in the hope that in this way a basis may be found for the calculation of important values. The difficulties encounter are of a twofold nature: in part they are connected with the complicated geometrical character of the hydrodynamical equations; in part they are dependent upon the presence of nonlinear terms, containing derivatives of the first order of the velocity components, along with derivatives of the second order multiplied by the very small coefficient of viscosity. The latter feature is responsible for a number of important, characteristics of turbulence, among which are prominent those connected with the balance of energy and with the appearance of dissipation layers. These layer an important part in the energy exchange, as they represent the main regions where energy is dissipated.

2,202 citations