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CEC
2007
IEEE
13 years 11 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 9 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 9 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 3 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 7 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