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CEC
2007
IEEE
14 years 1 months ago
SAT-decoding in evolutionary algorithms for discrete constrained optimization problems
— For complex optimization problems, several population-based heuristics like Multi-Objective Evolutionary Algorithms have been developed. These algorithms are aiming to deliver ...
Martin Lukasiewycz, Michael Glaß, Christian ...
EMO
2006
Springer
182views Optimization» more  EMO 2006»
13 years 11 months ago
Multi-objective Pole Placement with Evolutionary Algorithms
Multi-Objective Evolutionary Algorithms (MOEA) have been succesfully applied to solve control problems. However, many improvements are still to be accomplished. In this paper a new...
Gustavo Sánchez, Minaya Villasana, Miguel S...
GECCO
2009
Springer
162views Optimization» more  GECCO 2009»
13 years 12 months ago
TestFul: using a hybrid evolutionary algorithm for testing stateful systems
This paper introduces TestFul, a framework for testing stateful systems and focuses on object-oriented software. TestFul employs a hybrid multi-objective evolutionary algorithm, t...
Matteo Miraz, Pier Luca Lanzi, Luciano Baresi
ICST
2010
IEEE
13 years 5 months ago
TestFul: An Evolutionary Test Approach for Java
Abstract—This paper presents TestFul, an evolutionary testing approach for Java classes that works both at class and method level. TestFul exploits a multi-objective evolutionary...
Luciano Baresi, Pier Luca Lanzi, Matteo Miraz
AIIDE
2008
13 years 9 months ago
Constructing Complex NPC Behavior via Multi-Objective Neuroevolution
It is difficult to discover effective behavior for NPCs automatically. For instance, evolutionary methods can learn sophisticated behaviors based on a single objective, but realis...
Jacob Schrum, Risto Miikkulainen