Category: Evolutionary Intelligence

A parallel immune optimization algorithm for numeric function optimization

Abstract  Immune optimization algorithms show good performance in obtaining optimal solutions especially in dealing with numeric optimization
problems where such solutions are often difficult to determine by traditional techniques. This arti…

Abstract  Immune optimization algorithms show good performance in obtaining optimal solutions especially in dealing with numeric optimization
problems where such solutions are often difficult to determine by traditional techniques. This article presents the parallel
suppression control algorithm (PSCA), a parallel algorithm for optimization based on artificial immune systems (AIS). PSCA
is implemented in a parallel platform where the corresponding population of antibodies is partitioned into subpopulations
that are distributed among the processes. Each process executes the immunity-based algorithm for optimizing its subpopulation.
In the process of evolving the solutions, the activities of antibodies and the activities of the computation agents are regulated
by the general suppression control framework (GSCF) which maintains and controls the interactions between the populations
and processes. The proposed algorithm is evaluated with benchmark problems, and its performance is measured and compared with
other conventional optimization approaches.

  • Content Type Journal Article
  • DOI 10.1007/s12065-008-0014-8
  • Authors
    • Henry Y. K. Lau, The University of Hong Kong Department of Industrial and Manufacturing Systems Engineering Pokfulam Road Hong Kong, People’s Republic of China
    • Wilburn W. P. Tsang, The University of Hong Kong Department of Industrial and Manufacturing Systems Engineering Pokfulam Road Hong Kong, People’s Republic of China

Genetic-based machine learning systems are competitive for pattern recognition

Abstract  During the last decade, research on Genetic-Based Machine Learning has resulted in several proposals of supervised learning methodologies that use evolutionary algorithms to evolve rule-based classification models. Usually, these ne…

Abstract  During the last decade, research on Genetic-Based Machine Learning has resulted in several proposals of supervised learning methodologies that use evolutionary algorithms to evolve rule-based classification models. Usually, these new GBML approaches are accompanied by little experimentation
and there is a lack of comparisons among different proposals. Besides, the competitiveness of GBML systems with respect to
non-evolutionary, highly-used machine learning techniques has only been partially studied. This paper reviews the state of
the art in GBML, selects some of the best representatives of different families, and compares the accuracy and the interpretability
of their models. The paper also analyzes the behavior of the GBML approaches with respect to some of the most influential
machine learning techniques that belong to different learning paradigms such as decision trees, support vector machines, instance-based
classifiers, and probabilistic classifiers. The experimental observations emphasize the suitability of GBML systems for performing
classification tasks. Moreover, the analysis points out the strengths of the different systems, which can be used as recommendation
guidelines on which systems should be employed depending on whether the user prefers to maximize the accuracy or the interpretability
of the models.

  • Content Type Journal Article
  • DOI 10.1007/s12065-008-0013-9
  • Authors
    • Albert Orriols-Puig, Universitat Ramon Llull Grup de Recerca en Sistemes Intel·ligents, Enginyeria i Arquitectura La Salle Quatre Camins 2 08022 Barcelona Spain
    • Jorge Casillas, University of Granada Department of Computer Science and Artificial Intelligence 18071 Granada Spain
    • Ester Bernadó-Mansilla, Universitat Ramon Llull Grup de Recerca en Sistemes Intel·ligents, Enginyeria i Arquitectura La Salle Quatre Camins 2 08022 Barcelona Spain

The DCA: SOMe comparison

Abstract  The dendritic cell algorithm (DCA) is an immune-inspired algorithm, developed for the purpose of anomaly detection. The algorithm
performs multi-sensor data fusion and correlation which results in a ‘context aware’ detection sy…

Abstract  The dendritic cell algorithm (DCA) is an immune-inspired algorithm, developed for the purpose of anomaly detection. The algorithm
performs multi-sensor data fusion and correlation which results in a ‘context aware’ detection system. Previous applications
of the DCA have included the detection of potentially malicious port scanning activity, where it has produced high rates of
true positives and low rates of false positives. In this work we aim to compare the performance of the DCA and of a self-organizing
map (SOM) when applied to the detection of SYN port scans, through experimental analysis. A SOM is an ideal candidate for
comparison as it shares similarities with the DCA in terms of the data fusion method employed. It is shown that the results
of the two systems are comparable, and both produce false positives for the same processes. This shows that the DCA can produce
anomaly detection results to the same standard as an established technique.

  • Content Type Journal Article
  • DOI 10.1007/s12065-008-0008-6
  • Authors
    • Julie Greensmith, University of Nottingham School of Computer Science Wollaton Road Nottingham NG8 1BB UK
    • Jan Feyereisl, University of Nottingham School of Computer Science Wollaton Road Nottingham NG8 1BB UK
    • Uwe Aickelin, University of Nottingham School of Computer Science Wollaton Road Nottingham NG8 1BB UK

Evolutionary algorithms to simulate the phylogenesis of a binary artificial immune system

Abstract  Four binary-encoded models describing some aspects of the phylogenetics evolution in an artificial immune system have been
proposed and analyzed. The first model has focused on the evolution of a paratope’s population, considerin…

Abstract  Four binary-encoded models describing some aspects of the phylogenetics evolution in an artificial immune system have been
proposed and analyzed. The first model has focused on the evolution of a paratope’s population, considering a fixed group
of epitopes, to simulate a hypermutation mechanism and observe how the system would self-adjust to cover the epitopes. In
the second model, the evolution involves a group of antibodies adapting to a given antigenic molecules’ population. The third
model simulated the coevolution between antibodies’ generating gene libraries and antigens. The objective was to simulate
somatic recombination mechanisms to obtain final libraries apt to produce antibodies to cover any possible antigen that would
appear in the pathogens’ population. In the fourth model, the coevolution involves a new population of self-molecules whose
function was to establish restrictions in the evolution of libraries’ population. For all the models implemented, evolutionary
algorithms (EA) were used to form adaptive niching inspired in the coevolutionary shared niching strategy ideas taken from
a monopolistic competition economic model where “businessmen” locate themselves among geographically distributed “clients”
so as to maximize their profit. Numerical experiments and conclusions are shown. These considerations present many similarities
to biological immune systems and also some inspirations to solve real-world problems, such as pattern recognition and knowledge
discovery in databases.

  • Content Type Journal Article
  • DOI 10.1007/s12065-008-0010-z
  • Authors
    • Grazziela P. Figueredo, Federal University of Rio de Janeiro – COPPE Rio de Janeiro Brazil
    • Luis A. V. de Carvalho, Federal University of Rio de Janeiro – COPPE Rio de Janeiro Brazil
    • Helio J. C. Barbosa, LNCC, MCT Petrόpolis Brazil
    • Nelson F. F. Ebecken, Federal University of Rio de Janeiro – COPPE Rio de Janeiro Brazil

Exploratory data analysis with artificial immune systems

Abstract  We use a modified version of the CLONALG algorithm to perform exploratory data analysis. Since we wish to compare results
from a number of methods, we only report on linear projections which have unique solutions. We incorporate a …

Abstract  We use a modified version of the CLONALG algorithm to perform exploratory data analysis. Since we wish to compare results
from a number of methods, we only report on linear projections which have unique solutions. We incorporate a type of Gram
Schmidt orthogonalisation [15] into the affinity maturation process to capture multiple components. We combine the new algorithm with reinforcement learning
[17, 20] and with cross entropy maximization [13, 19]. Finally we combine several different non-standard adaptation methods using bagging and show that we get reliable convergence
to accurate filters.

  • Content Type Journal Article
  • DOI 10.1007/s12065-008-0012-x
  • Authors
    • Ying Wu, The University of the West of Scotland School of Computing Paisley Scotland
    • Colin Fyfe, The University of the West of Scotland School of Computing Paisley Scotland

Improving the reliability of real-time embedded systems using innate immune techniques

Abstract  Previous work has shown that immune-inspired techniques have good potential for solving problems associated with the development
of real-time embedded systems (RTES), where for various reasons traditional real-time development tech…

Abstract  Previous work has shown that immune-inspired techniques have good potential for solving problems associated with the development
of real-time embedded systems (RTES), where for various reasons traditional real-time development techniques are not suitable.
This paper examines in more detail the general applicability of the Dendritic Cell Algorithm (DCA) to the problem of task
scheduling in RTES. To make this possible, an understanding of the problem characteristics is formalised, such that the results
produced by the DCA can be examined in relation to the overall problem difficulty. The paper then contains a detailed understanding
of how well the DCA which demonstrates that it generally performs well, however it clearly identifies properties of anomalies
that are difficult to detect. These properties are as anticipated based on real-time scheduling theory.

  • Content Type Journal Article
  • DOI 10.1007/s12065-008-0009-5
  • Authors
    • Nicholas Lay, University of York Department of Computer Science York UK
    • Iain Bate, University of York Department of Computer Science York UK

Frequency analysis for dendritic cell population tuning

Abstract  The dendritic cell algorithm (DCA) has been applied successfully to a diverse range of applications. These applications are
related by the inherent uncertainty associated with sensing the application environment. The DCA has perfor…

Abstract  The dendritic cell algorithm (DCA) has been applied successfully to a diverse range of applications. These applications are
related by the inherent uncertainty associated with sensing the application environment. The DCA has performed well using
unfiltered signals from each environment as inputs. In this paper we demonstrate that the DCA has an emergent filtering mechanism
caused by the manner in which the cell accumulates its internal variables. Furthermore we demonstrate a relationship between
the migration threshold of the cells and the transfer function of the algorithm. A tuning methodology is proposed and a robotic
application published previously is revisited using the new tuning technique.

  • Content Type Journal Article
  • DOI 10.1007/s12065-008-0011-y
  • Authors
    • Robert Oates, University of Nottingham School of Computer Science Jubilee Campus, Wollaton Road Nottingham NG8 1BB UK
    • Graham Kendall, University of Nottingham School of Computer Science Jubilee Campus, Wollaton Road Nottingham NG8 1BB UK
    • Jonathan M. Garibaldi, University of Nottingham School of Computer Science Jubilee Campus, Wollaton Road Nottingham NG8 1BB UK

Special issue on artificial immune systems

Special issue on artificial immune systems
Content Type Journal ArticleDOI 10.1007/s12065-008-0007-7Authors
Uwe Aickelin, University of Nottingham School of Computer Science Nottingham UK

Journal Evolutionary Intelligence Online ISSN 186…

Special issue on artificial immune systems

  • Content Type Journal Article
  • DOI 10.1007/s12065-008-0007-7
  • Authors
    • Uwe Aickelin, University of Nottingham School of Computer Science Nottingham UK

Learning classifier systems: then and now

Abstract  Broadly conceived as computational models of cognition and tools for modeling complex adaptive systems, later extended for
use in adaptive robotics, and today also applied to effective classification and data-mining–what has happ…

Abstract  Broadly conceived as computational models of cognition and tools for modeling complex adaptive systems, later extended for
use in adaptive robotics, and today also applied to effective classification and data-mining–what has happened to learning
classifier systems in the last decade? This paper addresses this question by examining the current state of learning classifier
system research.

  • Content Type Journal Article
  • DOI 10.1007/s12065-007-0003-3
  • Authors
    • Pier Luca Lanzi, Politecnico di Milano Dipartimento di Elettronica e Informazione P.za L. da Vinci 32 20133 Milan Italy

Genetic fuzzy systems: taxonomy, current research trends and prospects

Abstract  The use of genetic algorithms for designing fuzzy systems provides them with the learning and adaptation capabilities and
is called genetic fuzzy systems (GFSs). This topic has attracted considerable attention in the Computation In…

Abstract  The use of genetic algorithms for designing fuzzy systems provides them with the learning and adaptation capabilities and
is called genetic fuzzy systems (GFSs). This topic has attracted considerable attention in the Computation Intelligence community
in the last few years. This paper gives an overview of the field of GFSs, being organized in the following four parts: (a)
a taxonomy proposal focused on the fuzzy system components involved in the genetic learning process; (b) a quick snapshot
of the GFSs status paying attention to the pioneer GFSs contributions, showing the GFSs visibility at ISI Web of Science including the most cited papers and pointing out the milestones covered by the books and the special issues in the topic;
(c) the current research lines together with a discussion on critical considerations of the recent developments; and (d) some
potential future research directions.

  • Content Type Journal Article
  • DOI 10.1007/s12065-007-0001-5
  • Authors
    • Francisco Herrera, University of Granada Department of Computer Science and Artificial Intelligence 18071 Granada Spain