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GECCO
2004
Springer
108views Optimization» more  GECCO 2004»
14 years 1 months ago
Simple Population Replacement Strategies for a Steady-State Multi-objective Evolutionary Algorithm
This paper explores some simple evolutionary strategies for an elitist, steady-state Pareto-based multi-objective evolutionary algorithm. The experimental framework is based on the...
Christine L. Mumford
GECCO
2010
Springer
176views Optimization» more  GECCO 2010»
13 years 10 months ago
A hierarchical cooperative evolutionary algorithm
To successfully search multiple coadaptive subcomponents in a solution, we developed a novel cooperative evolutionary algorithm based on a new computational multilevel selection f...
Shelly Xiaonan Wu, Wolfgang Banzhaf
GECCO
2000
Springer
142views Optimization» more  GECCO 2000»
13 years 11 months ago
Improving EAs for Sequencing Problems
Sequencing problems have to be solved very often in VLSI CAD. To obtain results of high quality, Evolutionary Algorithms (EAs) have been successfully applied in many cases. Howeve...
Wolfgang Günther, Rolf Drechsler
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
2006
Springer
162views Optimization» more  GECCO 2006»
13 years 11 months ago
Improving evolutionary real-time testing
Embedded systems are often used in a safety-critical context, e.g. in airborne or vehicle systems. Typically, timing constraints must be satisfied so that real-time embedded syste...
Marouane Tlili, Stefan Wappler, Harmen Sthamer