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» On the Brittleness of Evolutionary Algorithms
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GECCO
2003
Springer
148views Optimization» more  GECCO 2003»
15 years 11 months ago
Structural and Functional Sequence Test of Dynamic and State-Based Software with Evolutionary Algorithms
Evolutionary Testing (ET) has been shown to be very successful for testing real world applications [10]. The original ET approach focusesonsearching for a high coverage of the test...
André Baresel, Hartmut Pohlheim, Sadegh Sad...
GECCO
2006
Springer
134views Optimization» more  GECCO 2006»
15 years 9 months ago
Reference point based multi-objective optimization using evolutionary algorithms
: Evolutionary multi-objective optimization (EMO) methodologies have been amply applied to find a representative set of Pareto-optimal solutions in the past decade and beyond. Alth...
Kalyanmoy Deb, J. Sundar
BMCBI
2010
172views more  BMCBI 2010»
15 years 6 months ago
Comparison of evolutionary algorithms in gene regulatory network model inference
Background: The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineeri...
Alina Sîrbu, Heather J. Ruskin, Martin Crane
GECCO
2007
Springer
181views Optimization» more  GECCO 2007»
16 years 8 days ago
Exploring selection mechanisms for an agent-based distributed evolutionary algorithm
In this paper we propose an agent-based model of evolutionary algorithms (EAs) which extends seamlessly from concurrent single-host to distributed multi-host installations. Since ...
A. E. Eiben, Marc Schoenauer, Juan Luís Jim...
SYNASC
2005
IEEE
110views Algorithms» more  SYNASC 2005»
15 years 11 months ago
Performance Tuning of Evolutionary Algorithms Using Particle Sub Swarms
Particle Swarm Optimization (PSO) technique proved its ability to deal with very complicated optimization and search problems. This paper proposes a new particle swarm variant whi...
Crina Grosan, Ajith Abraham, Monica Nicoara