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GECCO
2008
Springer
199views Optimization» more  GECCO 2008»
13 years 8 months ago
Analysis of multi-objective evolutionary algorithms to optimize dynamic data types in embedded systems
New multimedia embedded applications are increasingly dynamic, and rely on Dynamically-allocated Data Types (DDTs) to store their data. The optimization of DDTs for each target em...
José Ignacio Hidalgo, José L. Risco-...
EMO
2009
Springer
143views Optimization» more  EMO 2009»
13 years 4 months ago
Adapting to the Habitat: On the Integration of Local Search into the Predator-Prey Model
Traditionally, Predator-Prey Models--although providing a more nature-oriented approach to multi-objective optimization than many other standard Evolutionary Multi-Objective Algori...
Christian Grimme, Joachim Lepping, Alexander Papas...
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 ...
GECCO
2006
Springer
179views Optimization» more  GECCO 2006»
13 years 10 months ago
Comparison of multi-objective evolutionary algorithms in optimizing combinations of reinsurance contracts
Our paper concerns optimal combinations of different types of reinsurance contracts. We introduce a novel approach based on the Mean-Variance-Criterion to solve this task. Two sta...
Ingo Oesterreicher, Andreas Mitschele, Frank Schlo...
HIS
2007
13 years 8 months ago
Pareto-based Multi-Objective Machine Learning
—Machine learning is inherently a multiobjective task. Traditionally, however, either only one of the objectives is adopted as the cost function or multiple objectives are aggreg...
Yaochu Jin