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EMO
2005
Springer
194views Optimization» more  EMO 2005»
15 years 9 months ago
An EMO Algorithm Using the Hypervolume Measure as Selection Criterion
Abstract. The hypervolume measure is one of the most frequently applied measures for comparing the results of evolutionary multiobjective optimization algorithms (EMOA). The idea t...
Michael Emmerich, Nicola Beume, Boris Naujoks
CEC
2009
IEEE
15 years 10 months ago
Evolving modular neural-networks through exaptation
— Despite their success as optimization methods, evolutionary algorithms face many difficulties to design artifacts with complex structures. According to paleontologists, living...
Jean-Baptiste Mouret, Stéphane Doncieux
GECCO
2004
Springer
116views Optimization» more  GECCO 2004»
15 years 9 months ago
Reducing Fitness Evaluations Using Clustering Techniques and Neural Network Ensembles
Abstract. In many real-world applications of evolutionary computation, it is essential to reduce the number of fitness evaluations. To this end, computationally efficient models c...
Yaochu Jin, Bernhard Sendhoff
PPSN
1998
Springer
15 years 8 months ago
A Spatial Predator-Prey Approach to Multi-objective Optimization: A Preliminary Study
This paper presents a novel evolutionary approach of approximating the shape of the Pareto-optimal set of multi-objective optimization problems. The evolutionary algorithm (EA) use...
Marco Laumanns, Günter Rudolph, Hans-Paul Sch...
ICAC
2009
IEEE
15 years 10 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. ...