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» On the Brittleness of Evolutionary Algorithms
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CEC
2009
IEEE
16 years 1 months ago
Towards creative design using collaborative interactive genetic algorithms
— We present a computational model of creative design based on collaborative interactive genetic algorithms. We test our model on floorplanning. We guide the evolution of floor...
Juan C. Quiroz, Sushil J. Louis, Amit Banerjee, Se...
EPIA
2007
Springer
16 years 19 days ago
Evaluating Simulated Annealing Algorithms in the Optimization of Bacterial Strains
Abstract. In this work, a Simulated Annealing (SA) algorithm is proposed for a Metabolic Engineering task: the optimization of the set of gene deletions to apply to a microbial str...
Miguel Rocha, Rui Mendes, Paulo Maia, José ...
GECCO
2005
Springer
152views Optimization» more  GECCO 2005»
16 years 7 hour ago
GAMM: genetic algorithms with meta-models for vision
Recent adaptive image interpretation systems can reach optimal performance for a given domain via machine learning, without human intervention. The policies are learned over an ex...
Greg Lee, Vadim Bulitko
GECCO
2004
Springer
131views Optimization» more  GECCO 2004»
15 years 12 months ago
PolyEDA: Combining Estimation of Distribution Algorithms and Linear Inequality Constraints
Estimation of distribution algorithms (EDAs) are population-based heuristic search methods that use probabilistic models of good solutions to guide their search. When applied to co...
Jörn Grahl, Franz Rothlauf
GECCO
2007
Springer
124views Optimization» more  GECCO 2007»
15 years 10 months ago
Fitness-proportional negative slope coefficient as a hardness measure for genetic algorithms
The Negative Slope Coefficient (nsc) is an empirical measure of problem hardness based on the analysis of offspring-fitness vs. parent-fitness scatterplots. The nsc has been teste...
Riccardo Poli, Leonardo Vanneschi