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Volume 3, Number 1, 2001
A Special Issue on Evolutionary Multicriteria Optimization
ABSTRACTS
| A Multi-Objective Genetic Algorithm Approach to Feature Selection in Neural and Fuzzy Modeling |
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| Christos EMMANOUILIDIS, Andrew HUNTER, John MACINTYRE and Chris COX |
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Abstract :A large number of techniques, such a neural networks and neurofuzzy
systems, are used to produce empirical models based in part or in whole on
observed data. A key stage in the modelling process is the selection of features.
Irrelevant or noisy features increase the complexity of the modelling problem, may
introduce additional costs in gathering unneeded data, and frequently degrade
modelling performance. Often it is acceptable to trade off some decrease in
performance against a reduction in complexity (number of input features), although
we rarely know a priori what an acceptable trade-off is. In this paper, feature
selection is posed as a multiobjective optimisation problem, as in the simplest
case it involves feature subset size minimisation and performance maximisation. We
propose multiobjective genetic algorithms as an effective means of evolving a
population of alternative feature subsets with various modelling
accuracy/complexity trade-offs, based on the concept of dominance. We discuss
methods to reduce the computational costs of the technique, including the use of
special forms of neural network and neurofuzzy models. The major contributions of
this paper are: the formulation of feature selection as a multiobjective
optimisation problem; the use of multiobjective evolutionary algorithms, based on
the concept of dominance, for multiobjective feature subset selection; and the
application of the multiobjective genetic algorithm feature selection on a number
of neural and fuzzy models together with fast subset evaluation techniques. By
considering both neural networks and neurofuzzy models, we show that our approach
can be generically applied to different modelling techniques. The proposed method
is applied on two small and high dimensional regression problems. |
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Keywords: Multi-objective evolutionary algorithms, Feature
Selection, Neurofuzzy modelling, Neural Networks |
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| Selection and Mutation Strategies in Evolutionary Algorithms for Global Multiobjective Optimization |
27 |
| Thomas HANNE |
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Abstract :This paper serves the discussion of some questions
concerning the application of evolutionary algorithms to multiobjective
optimization problems with continuous variables. A main question of
transforming evolutionary algorithms for scalar optimization into those for
multiobjective optimization concerns the modification of the selection step.
In earlier work we have analyzed specific properties of selection rules
called efficiency preservation and negative efficiency preservation. In this
article , we discuss the use of these properties by applying an accordingly
modified selection rule to some test problems. The number of efficient
alternatives of a population for different test problems provides a better
understanding of the change of data during the evolutionary process. Also
effects of the number of objective functions are treated. We also analyze
the influence of the number of objectives and the relevance of these results
in the context of the 1/5 rule, a mutation control concept for scalar
evolutionary algorithms which cannot easily be transformed into the
multiobjective case. |
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Keywords :Evolutionary Algorithms, Multicriteria Optimization,
Stochastic Search, Selection Mechanism, Step Sizes, 1/5 Rule. |
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| A Minimal Cost Hybrid Strategy for Pareto Optimal Front Approximation |
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| Marco FARINA |
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Abstract :A strategy is proposed for coarse grained Pareto Optimal
Front approximation. It is devoted to industrial design optimization
problems when the number of objective function calls that can be afforded
in a practical time is much lower than the number required for convergence
of available and powerful MOEAs. An hybrid evolutionary-deterministic and
global-local search is applied on a movable preference function derived
from L8 norm in objective domain. Both convergence and diversity of
solution is tested on several analytical functions. |
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Keywords :Pareto Optimal Front, Industrial Design, L8 Norm. |
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