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» Hybridizing Evolutionary Testing with the Chaining Approach
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CEC
2009
IEEE
14 years 2 months ago
Local search based evolutionary multi-objective optimization algorithm for constrained and unconstrained problems
Abstract— Evolutionary multi-objective optimization algorithms are commonly used to obtain a set of non-dominated solutions for over a decade. Recently, a lot of emphasis have be...
Karthik Sindhya, Ankur Sinha, Kalyanmoy Deb, Kaisa...
GECCO
2005
Springer
159views Optimization» more  GECCO 2005»
14 years 27 days ago
Using evolutionary algorithms for the unit testing of object-oriented software
As the paradigm of object orientation becomes more and more important for modern IT development projects, the demand for an automated test case generation to dynamically test obje...
Stefan Wappler, Frank Lammermann
CEC
2010
IEEE
13 years 4 months ago
Improving evolutionary testing by means of efficiency enhancement techniques
TestFul is a novel evolutionary testing approach for object-oriented programs with complex internal states. In our preliminary experiments, it already outperformed some of the well...
Matteo Miraz, Pier Luca Lanzi, Luciano Baresi
GECCO
2004
Springer
14 years 22 days ago
Towards a Generally Applicable Self-Adapting Hybridization of Evolutionary Algorithms
When applied to real-world problems, the powerful optimization tool of Evolutionary Algorithms frequently turns out to be too time-consuming due to elaborate fitness calculations t...
Wilfried Jakob, Christian Blume, Georg Bretthauer
HAIS
2009
Springer
13 years 12 months ago
Hybrid Evolutionary Algorithm for Solving Global Optimization Problems
Differential Evolution (DE) is a novel evolutionary approach capable of handling non-differentiable, non-linear and multi-modal objective functions. DE has been consistently ranked...
Radha Thangaraj, Millie Pant, Ajith Abraham, Youak...