Training Multilayer Perceptrons with a Gaussian Artificial Immune System   [MP] [AI] [AIS] [IS]

by

Castro, P. and Zuben, F., J., V.

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Info: Proceedings of the 2011 IEEE Congress on Evolutionary Computation (Conference proceedings), 2011, p. 1255-1262
Keywords:Evolved neural networks, Artificial immune systems, Estimation of distribution algorithms
Abstract:
In this paper we apply an immune-inspired approach to train Multilayer Perceptrons [MP] (MLPs) for classification problems. [CP] Our proposal, called Gaussian Artificial Immune System [AI] [AIS] [IS] (GAIS), is an estimation of distribution algorithm that replaces the traditional mutation and cloning operators with a probabilistic model, [PM] more specifically a Gaussian network, representing the joint distribution of promising solutions. Subsequently, GAIS utilizes this probabilistic model [PM] for sampling new solutions. Thus, the algorithm takes into account the relationships among the variables of the problem, avoiding the disruption of already obtained high-quality partial solutions (building blocks). [BB] Besides the capability to identify and manipulate building blocks, [BB] the algorithm maintains diversity in the population, performs multimodal optimization [MO] and adjusts the size of the population automatically according to the problem. These attributes are generally absent from alternative algorithms, and all were shown to be useful attributes when optimizing the weights of MLPs, thus guiding to high-performance classifiers. GAIS was evaluated in six well-known classification problems [CP] and its performance compares favorably with that produced by contenders, such as opt-aiNet, IDEA and PSO.
Notes:
CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.
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BibTex:
@InProceedings{Castro:2011:TMPwaGAIS,
  title     = {Training Multilayer Perceptrons with a  Gaussian Artificial Immune System},
  author    = {Pablo Castro and Fernando J. {Von Zuben}},
  pages     = {1255--1262},
  booktitle = {Proceedings of the 2011 IEEE Congress on Evolutionary Computation},
  year      = {2011},
  editor = "Alice E. Smith",
  month     = {5-8 June},
  address   = {New Orleans, USA},
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  ISBN      = {0-7803-8515-2},
  keywords  = {Evolved neural networks, Artificial immune systems, Estimation of distribution algorithms},
  abstract  = {
In this paper we apply an immune-inspired approach to train  Multilayer
Perceptrons (MLPs)  for classification problems. Our proposal, called Gaussian
Artificial Immune System (GAIS), is an estimation of distribution algorithm that
replaces the traditional mutation and cloning operators with a probabilistic
model, more specifically a Gaussian network, representing the joint distribution
of promising solutions.
Subsequently, GAIS utilizes this probabilistic model for sampling new solutions.
Thus, the algorithm takes into account the relationships among the variables of
the problem, avoiding the disruption of already obtained high-quality partial
solutions (building blocks).
Besides the capability to identify and manipulate building blocks, the algorithm
maintains diversity in the population, performs multimodal optimization and
adjusts the size of the population automatically according to the problem.
 These attributes are generally absent from alternative algorithms, and all were
shown to be useful attributes when optimizing the weights of MLPs, thus guiding
to high-performance classifiers.  GAIS was evaluated in six well-known
classification problems and its performance compares favorably with that
produced by contenders, such as opt-aiNet, IDEA and PSO.
},
  notes =	{CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET. },
}


% Special Session: Evolution of Developmental and Generative Systems