Threshold selection - a selection mechanism for noisy evolutionary algorithms - is put into the broader context of hypothesis testing. Theoretical results are presented and applied to a simple model of stochastic search and to a simplified elevator simulator. Design of experiments methods are used to validate the significance of the results
Evolutionary algorithm (EA) is an umbrella term used to describe population-based stochastic direct ...
Evolutionary computation (EC) is a relatively new discipline in computer science (Eiben & Smith, 200...
In genetic algorithms selection mechanisms aim to favour reproduction of better individuals imposing...
Many optimization tasks have to be handled in noisy environments, where we cannot obtain the exact e...
With the rise in the application of evolution strategies for simulation optimization, a better under...
A theoretical model is presented which describes selection in a genetic algorithm (GA) under a stoch...
Evolution strategies are general, nature-inspired heuristics for search and optimization. Supported ...
Selection methods in Evolutionary Algorithms, including Genetic Algorithms, Evolution Strategies (ES...
Theoretical analyses of stochastic search algorithms, albeit few, have always existed since these al...
Selection functions enable Evolutionary Algorithms (EAs) to apply selection pressure to a population...
This paper examines how the choice of the selection mech-anism in an evolutionary algorithm impacts ...
This book develops efficient methods for the application of Evolutionary Algorithms on stochastic pr...
A theoretical model is presented which describes selection in a genetic algorithm (GA) under a stoch...
A hybrid approach that combines the (1+1)-ES and threshold selection methods is developed. The fram...
Selection functions enable Evolutionary Algorithms (EAs) to apply selection pressure to a population...
Evolutionary algorithm (EA) is an umbrella term used to describe population-based stochastic direct ...
Evolutionary computation (EC) is a relatively new discipline in computer science (Eiben & Smith, 200...
In genetic algorithms selection mechanisms aim to favour reproduction of better individuals imposing...
Many optimization tasks have to be handled in noisy environments, where we cannot obtain the exact e...
With the rise in the application of evolution strategies for simulation optimization, a better under...
A theoretical model is presented which describes selection in a genetic algorithm (GA) under a stoch...
Evolution strategies are general, nature-inspired heuristics for search and optimization. Supported ...
Selection methods in Evolutionary Algorithms, including Genetic Algorithms, Evolution Strategies (ES...
Theoretical analyses of stochastic search algorithms, albeit few, have always existed since these al...
Selection functions enable Evolutionary Algorithms (EAs) to apply selection pressure to a population...
This paper examines how the choice of the selection mech-anism in an evolutionary algorithm impacts ...
This book develops efficient methods for the application of Evolutionary Algorithms on stochastic pr...
A theoretical model is presented which describes selection in a genetic algorithm (GA) under a stoch...
A hybrid approach that combines the (1+1)-ES and threshold selection methods is developed. The fram...
Selection functions enable Evolutionary Algorithms (EAs) to apply selection pressure to a population...
Evolutionary algorithm (EA) is an umbrella term used to describe population-based stochastic direct ...
Evolutionary computation (EC) is a relatively new discipline in computer science (Eiben & Smith, 200...
In genetic algorithms selection mechanisms aim to favour reproduction of better individuals imposing...
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