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ACMSE
2006
ACM
14 years 1 months ago
Using genetic algorithms to generate test plans for functionality testing
Like in other fields, computer products (applications, hardware, etc.), before being marketed, require some level of testing to verify whether they meet their design and function...
Francisca Emanuelle Vieira, Francisco Martins, Raf...
GECCO
2008
Springer
111views Optimization» more  GECCO 2008»
13 years 8 months ago
Single-objective front optimization: application to rf circuit design
This paper proposes a new algorithm which promotes well distributed non-dominated fronts in the parameters space when a single-objective function is optimized. This algorithm is b...
Eduardo José Solteiro Pires, Luís Me...
ICAC
2009
IEEE
14 years 2 months ago
Applying genetic algorithms to decision making in autonomic computing systems
Increasingly, applications need to be able to self-reconfigure in response to changing requirements and environmental conditions. Autonomic computing has been proposed as a means...
Andres J. Ramirez, David B. Knoester, Betty H. C. ...
IJAR
2010
153views more  IJAR 2010»
13 years 6 months ago
Diagnosis of dyslexia with low quality data with genetic fuzzy systems
For diagnosing dyslexia in early childhood, children have to solve non-writing based, graphical tests. Curently, these tests are processed by a human expert; applying artificial ...
Ana M. Palacios, Luciano Sánchez, Iné...
GECCO
2008
Springer
172views Optimization» more  GECCO 2008»
13 years 8 months ago
Empirical analysis of a genetic algorithm-based stress test technique
Evolutionary testing denotes the use of evolutionary algorithms, e.g., Genetic Algorithms (GAs), to support various test automation tasks. Since evolutionary algorithms are heuris...
Vahid Garousi