This article presents statistical techniques for the design and analysis of evolution strategies. These techniques can be applied to other search heuristics such as genetic algorithms, simulated annealing or particle swarm optimizers. It provides guidelines for the comparison of di#erent algorithms on artifical test functions and on real-world optimization problems. Statistical experimental design techniques to improve the integrity and comparability of experiments are proposed. Interpreting the run of an optimization algorithm as an experiment, design of experiments (DOE), response surface methods (RSM), and tree-based regression methods can be applied to analyze and to improve its performance
Abstract: Experimental analysis starts with very similar premises: given a specific problem, we need...
With the rise in the application of evolution strategies for simulation optimization, a better under...
This book is intended as a reference both for experienced users of evolutionary algorithms and for r...
In empirical studies of Evolutionary Algorithms, it is usually desirable to evaluate and compare alg...
International audienceThis paper describes a statistical method that helps to find good parameter se...
Despite the continuous advancement of Evolutionary Algorithms (EAs) and their numerous successful ap...
In operations research and computer science it is common practice to evaluate the performance of opt...
International audienceEvolution strategies are evolutionary algorithms that date back to the 1960s a...
Classical test functions known as F1 to F10 are often used as a benchmark for heuristic optimisation...
The use of numerical optimization techniques on simulation models is a developing field. Many of the...
A brief discussion of the genesis of evolutionary computation methods, their relationship to artific...
The use of numerical optimization techniques on simulation models is a developing field. Many of the...
Originally Evolution Strategies (ESs) have been developed for experimental optimization, i.e. optimi...
Evolutionary algorithm (EA) is an umbrella term used to describe population-based stochastic direct ...
Optimized operating conditions for complex systems can be attained by using advanced combinations of...
Abstract: Experimental analysis starts with very similar premises: given a specific problem, we need...
With the rise in the application of evolution strategies for simulation optimization, a better under...
This book is intended as a reference both for experienced users of evolutionary algorithms and for r...
In empirical studies of Evolutionary Algorithms, it is usually desirable to evaluate and compare alg...
International audienceThis paper describes a statistical method that helps to find good parameter se...
Despite the continuous advancement of Evolutionary Algorithms (EAs) and their numerous successful ap...
In operations research and computer science it is common practice to evaluate the performance of opt...
International audienceEvolution strategies are evolutionary algorithms that date back to the 1960s a...
Classical test functions known as F1 to F10 are often used as a benchmark for heuristic optimisation...
The use of numerical optimization techniques on simulation models is a developing field. Many of the...
A brief discussion of the genesis of evolutionary computation methods, their relationship to artific...
The use of numerical optimization techniques on simulation models is a developing field. Many of the...
Originally Evolution Strategies (ESs) have been developed for experimental optimization, i.e. optimi...
Evolutionary algorithm (EA) is an umbrella term used to describe population-based stochastic direct ...
Optimized operating conditions for complex systems can be attained by using advanced combinations of...
Abstract: Experimental analysis starts with very similar premises: given a specific problem, we need...
With the rise in the application of evolution strategies for simulation optimization, a better under...
This book is intended as a reference both for experienced users of evolutionary algorithms and for r...
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