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

Evolving encapsulated programs as shared grammars

Abstract  Facilitating the discovery and reuse of modular building blocks is generally regarded as the key to achieving better scalability
in genetic programming (GP). A precedent for this exists in biology, where complex designs are the pro…

Abstract  Facilitating the discovery and reuse of modular building blocks is generally regarded as the key to achieving better scalability
in genetic programming (GP). A precedent for this exists in biology, where complex designs are the product of developmental
processes that can also be abstractly modeled as generative grammars. We introduce shared grammar evolution (SGE), which aligns
grammatical development with the common application of grammars in GP as a means of establishing declarative bias. Programs
are derived from and represented by a global context-free grammar that is transformed and extended according to another, user-defined
grammar. Grammatical productions and the subroutines they encapsulate are shared between programs, which enables their reuse
without reevaluation and can significantly reduce total evaluation time for large programs and populations. Several variants
of SGE employing different strategies for controlling solution size and diversity are tested on classic GP problems. Results
compare favorably against GP and newer techniques, with the best results obtained by promoting diversity between derived programs.

  • Content Type Journal Article
  • Category Original Paper
  • DOI 10.1007/s10710-008-9061-2
  • Authors
    • Martin H. Luerssen, Flinders University of South Australia School of Informatics and Engineering GPO Box 2100 Adelaide 5001 Australia
    • David M. W. Powers, Flinders University of South Australia School of Informatics and Engineering GPO Box 2100 Adelaide 5001 Australia

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

An eigen analysis of the GP community

Abstract  The coauthorship and coeditorship relations as recorded in the genetic programming bibliography provide a quantitative view
of the GP community. Eigen analysis is used to find the principle components of the community. It shows the major eigenvalues
and eigenvectors are responsible for 70% of the connection graph. Top eigen authors are given.

  • Content Type Journal Article
  • Category Original Paper
  • DOI 10.1007/s10710-008-9060-3
  • Authors
    • W. B. Langdon, University of Essex Departments of Biological and Mathematical Sciences Colchester UK
    • R. Poli, University of Essex Department of Computing and Electronic Systems Colchester UK
    • W. Banzhaf, Memorial University of Newfoundland Department of Computer Science St.John’s Canada

Abstract  The coauthorship and coeditorship relations as recorded in the genetic programming bibliography provide a quantitative view
of the GP community. Eigen analysis is used to find the principle components of the community. It shows the major eigenvalues
and eigenvectors are responsible for 70% of the connection graph. Top eigen authors are given.

  • Content Type Journal Article
  • Category Original Paper
  • DOI 10.1007/s10710-008-9060-3
  • Authors
    • W. B. Langdon, University of Essex Departments of Biological and Mathematical Sciences Colchester UK
    • R. Poli, University of Essex Department of Computing and Electronic Systems Colchester UK
    • W. Banzhaf, Memorial University of Newfoundland Department of Computer Science St.John’s Canada

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

Cyclic metamorphic memory for cellular bio-inspired electronic systems

Abstract  In nature the DNA contained in each cell of an organism, is in fact a memory map that, through the transcription of its genes,
describes the unique characteristics of the individual. Similarly in an artificial embryonic cell, used to construct electronic
systems, a memory map and its relevant gene also describes and determines the functionality of each cell and, collectively,
the entire system. This paper proposes a new variable size memory map based on a novel and efficient gene selection algorithm
that no longer uses the hitherto common address decoding approach to access some fixed memory space. Instead, it applies the
principle of cyclic metamorphic gene selection of the artificial DNA memory. A further benefit of the approach is that the
functionality of the system can also be easily altered through genetic operators or variable memory space environment for
enhanced behaviour. It is suitable therefore for the implementation of GA processes.

  • Content Type Journal Article
  • Category Originall Paper
  • DOI 10.1007/s10710-008-9056-z
  • Authors
    • M. Samie, Shiraz University Department of EE Engineering Shiraz Iran
    • E. Farjah, Shiraz University Department of EE Engineering Shiraz Iran
    • G. Dragffy, University of the West of England Bristol UK

Abstract  In nature the DNA contained in each cell of an organism, is in fact a memory map that, through the transcription of its genes,
describes the unique characteristics of the individual. Similarly in an artificial embryonic cell, used to construct electronic
systems, a memory map and its relevant gene also describes and determines the functionality of each cell and, collectively,
the entire system. This paper proposes a new variable size memory map based on a novel and efficient gene selection algorithm
that no longer uses the hitherto common address decoding approach to access some fixed memory space. Instead, it applies the
principle of cyclic metamorphic gene selection of the artificial DNA memory. A further benefit of the approach is that the
functionality of the system can also be easily altered through genetic operators or variable memory space environment for
enhanced behaviour. It is suitable therefore for the implementation of GA processes.

  • Content Type Journal Article
  • Category Originall Paper
  • DOI 10.1007/s10710-008-9056-z
  • Authors
    • M. Samie, Shiraz University Department of EE Engineering Shiraz Iran
    • E. Farjah, Shiraz University Department of EE Engineering Shiraz Iran
    • G. Dragffy, University of the West of England Bristol UK

Analysis of mass spectrometry data of cerebral stroke samples: an evolutionary computation approach to resolve and quantify peptide peaks

Abstract  A preliminary investigation of cerebral stroke samples injected into a mass spectrometer is performed from an evolutionary
computation perspective. The detection and resolution of peptide peaks is pursued for the purpose of automatically and accurately
determining unlabeled peptide quantities. A theoretical peptide peak model is proposed and a series of experiments are then
pursued (most within a distributed computing environment) along with a data preprocessing strategy that includes (i) a deisotoping
step followed by (ii) a peak picking procedure, followed by (iii) a series of evolutionary computation experiments oriented
towards the investigation of their capability for achieving the aforementioned goal. Results from four different genetic algorithms
(GA) and one differential evolution (DE) algorithm are reported with respect to their ability to find solutions that fit within
the framework of the presented theoretical peptide peak model. Both unconstrained and constrained (as determined by a course
grained preprocessing stage) solution space experiments are performed for both types of evolutionary algorithms. Good preliminary
results are obtained.

  • Content Type Journal Article
  • Category Original Paper
  • DOI 10.1007/s10710-008-9057-y
  • Authors
    • Julio J. Valdés, National Research Council Canada Institute for Information Technology Bldg. M-50, 1200 Montreal Rd. Ottawa ON Canada K1A 0R6
    • Alan J. Barton, National Research Council Canada Institute for Information Technology Bldg. M-50, 1200 Montreal Rd. Ottawa ON Canada K1A 0R6
    • Arsalan S. Haqqani, National Research Council Canada Institute for Biological Sciences 100 Sussex Dr. Ottawa ON Canada K1A 0R6

Abstract  A preliminary investigation of cerebral stroke samples injected into a mass spectrometer is performed from an evolutionary
computation perspective. The detection and resolution of peptide peaks is pursued for the purpose of automatically and accurately
determining unlabeled peptide quantities. A theoretical peptide peak model is proposed and a series of experiments are then
pursued (most within a distributed computing environment) along with a data preprocessing strategy that includes (i) a deisotoping
step followed by (ii) a peak picking procedure, followed by (iii) a series of evolutionary computation experiments oriented
towards the investigation of their capability for achieving the aforementioned goal. Results from four different genetic algorithms
(GA) and one differential evolution (DE) algorithm are reported with respect to their ability to find solutions that fit within
the framework of the presented theoretical peptide peak model. Both unconstrained and constrained (as determined by a course
grained preprocessing stage) solution space experiments are performed for both types of evolutionary algorithms. Good preliminary
results are obtained.

  • Content Type Journal Article
  • Category Original Paper
  • DOI 10.1007/s10710-008-9057-y
  • Authors
    • Julio J. Valdés, National Research Council Canada Institute for Information Technology Bldg. M-50, 1200 Montreal Rd. Ottawa ON Canada K1A 0R6
    • Alan J. Barton, National Research Council Canada Institute for Information Technology Bldg. M-50, 1200 Montreal Rd. Ottawa ON Canada K1A 0R6
    • Arsalan S. Haqqani, National Research Council Canada Institute for Biological Sciences 100 Sussex Dr. Ottawa ON Canada K1A 0R6

Introduction to Evolvable Hardware: A Practical Guide for Designing Self-Adaptive Systems

Introduction to Evolvable Hardware: A Practical Guide for Designing Self-Adaptive Systems

  • Content Type Journal Article
  • Category Book Review
  • DOI 10.1007/s10710-008-9058-x
  • Authors
    • Antonio Mesquita, Programa de Engenharia Eletrica, COPPE/UFRJ CP. 68504, Centro de Tecnologia Ilha do Fundao CEP 21945-970 Rio de Janeiro RJ Brazil

Introduction to Evolvable Hardware: A Practical Guide for Designing Self-Adaptive Systems

  • Content Type Journal Article
  • Category Book Review
  • DOI 10.1007/s10710-008-9058-x
  • Authors
    • Antonio Mesquita, Programa de Engenharia Eletrica, COPPE/UFRJ CP. 68504, Centro de Tecnologia Ilha do Fundao CEP 21945-970 Rio de Janeiro RJ Brazil