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research article

Memetic Viability Evolution for Constrained Optimization

Maesani, Andrea  
•
Iacca, Giovanni  
•
Floreano, Dario  
2016
IEEE Transactions on Evolutionary Computation

The performance of evolutionary algorithms can be heavily undermined when constraints limit the feasible areas of the search space. For instance, while Covariance Matrix Adapta- tion Evolution Strategy is one of the most efficient algorithms for unconstrained optimization problems, it cannot be readily applied to constrained ones. Here, we used concepts from Memetic Computing, i.e. the harmonious combination of multiple units of algorithmic information, and Viability Evolution, an alternative abstraction of artificial evolution, to devise a novel approach for solving optimization problems with inequality constraints. Viability Evolution emphasizes elimination of solutions not satis- fying viability criteria, defined as boundaries on objectives and constraints. These boundaries are adapted during the search to drive a population of local search units, based on Covariance Matrix Adaptation Evolution Strategy, towards feasible regions. These units can be recombined by means of Differential Evolution operators. Of crucial importance for the performance of our method, an adaptive scheduler toggles between exploitation and exploration by selecting to advance one of the local search units and/or recombine them. The proposed algorithm can outperform several state-of-the-art methods on a diverse set of benchmark and engineering problems, both for quality of solutions and computational resources needed.

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Type
research article
DOI
10.1109/TEVC.2015.2428292
Web of Science ID

WOS:000370437600009

Author(s)
Maesani, Andrea  
Iacca, Giovanni  
Floreano, Dario  
Date Issued

2016

Publisher

Institute of Electrical and Electronics Engineers

Published in
IEEE Transactions on Evolutionary Computation
Volume

20

Issue

1

Start page

125

End page

144

Subjects

Constrained Optimization

•

Covariance Matrix Adaptation

•

Differential Evolution

•

Memetic Computing

•

Viability Evolution

•

Evolutionary Robotics

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LIS  
Available on Infoscience
April 27, 2015
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/113538
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