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» On the Brittleness of Evolutionary Algorithms
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EVOW
2010
Springer
15 years 5 months ago
Parameter Tuning of Evolutionary Algorithms: Generalist vs. Specialist
Abstract. Finding appropriate parameter values for Evolutionary Algorithms (EAs) is one of the persistent challenges of Evolutionary Computing. In recent publications we showed how...
Selmar K. Smit, A. E. Eiben
85
Voted
GECCO
2007
Springer
140views Optimization» more  GECCO 2007»
15 years 4 months ago
Peer-to-peer evolutionary algorithms with adaptive autonomous selection
In this paper we describe and evaluate a fully distributed P2P evolutionary algorithm (EA) with adaptive autonomous selection. Autonomous selection means that decisions regarding ...
W. R. M. U. K. Wickramasinghe, Maarten van Steen, ...
EVOW
2005
Springer
15 years 3 months ago
An Evolutionary Infection Algorithm for Dense Stereo Correspondence
This work presents an evolutionary approach to improve the infection algorithm to solve the problem of dense stereo matching. Dense stereo matching is used for 3D reconstruction in...
Cynthia B. Pérez, Gustavo Olague, Francisco...
85
Voted
GECCO
2009
Springer
161views Optimization» more  GECCO 2009»
15 years 4 months ago
Are evolutionary rule learning algorithms appropriate for malware detection?
In this paper, we evaluate the performance of ten well-known evolutionary and non-evolutionary rule learning algorithms. The comparative study is performed on a real-world classiï...
M. Zubair Shafiq, S. Momina Tabish, Muddassar Faro...
ISNN
2009
Springer
15 years 4 months ago
An Improved Quantum Evolutionary Algorithm with 2-Crossovers
Quantum evolutionary algorithm (QEA) is proposed on the basis of the concept and principles of quantum computing, which is a classical metaheuristic algorithm for the approximate s...
Zhihui Xing, Haibin Duan, Chunfang Xu