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Real-Parameter Black-Box Optimization Benchmarking 2009: Noiseless Functions Definitions
TLDR
The BBOB 2009 workshop will furnish most of this tedious task for its participants: (1) choice and implementation of a well-motivated real-parameter benchmark function testbed, (2) design of an experimental set-up, (3) generation of data output for (4) post-processing and presentation of the results in graphs and tables. Expand
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Real-Parameter Black-Box Optimization Benchmarking 2009: Experimental Setup
TLDR
Quantifying and comparing performance of optimization algorithms is one important aspect of research in search and optimization. Expand
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Real-Parameter Black-Box Optimization Benchmarking: Experimental Setup
Quantifying and comparing performance of numerical optimization algorithms is an important aspect of research in search and optimization. However, this task turns out to be tedious and difficult toExpand
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A Simple Modification in CMA-ES Achieving Linear Time and Space Complexity
TLDR
This paper proposes a simple modification of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for high dimensional objective functions, reducing the internal time and space complexity from quadratic to linear. Expand
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Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009
TLDR
This paper presents results of the BBOB-2009 benchmarking of 31 search algorithms on 24 noiseless functions in a black-box optimization scenario in continuous domain. Expand
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Real-Parameter Black-Box Optimization Benchmarking BBOB-2010 : Experimental Setup
Quantifying and comparing performance of numerical optimization algorithms is one important aspect of research in search and optimization. However, this task turns out to be tedious and difficult toExpand
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Benchmarking a weighted negative covariance matrix update on the BBOB-2010 noiseless testbed
TLDR
We implement a weighted negative update of the covariance matrix in the CMA-ES and compare the performance with the IPOP-CMA- ES on BBOB-2010 noiseless testbed in dimensions between 2 and 40. Expand
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Benchmarking the BFGS algorithm on the BBOB-2009 function testbed
TLDR
The BFGS quasi-Newton method is benchmarked on the noiseless BBOB-2009 testbed. Expand
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Benchmarking the NEWUOA on the BBOB-2009 function testbed
TLDR
The NEWUOA which belongs to the class of Derivative-Free optimization algorithms is benchmarked on the BBOB-2009 noisefree testbed. Expand
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Impacts of invariance in search: When CMA-ES and PSO face ill-conditioned and non-separable problems
TLDR
This paper investigates the behavior of PSO (particle swarm optimization) and CMA-ES (covariance matrix adaptation evolution strategy) on ill-conditioned functions. Expand
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