The well-known Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a robust stochastic search algorithm for optimizing functions defined on a continuous search space RD ....
In a companion paper, we presented a weighted negative update of the covariance matrix in the CMA-ES—weighted active CMA-ES or, in short, aCMA-ES. In this paper, we benchmark th...
Derandomization by means of mirrored samples has been recently introduced to enhance the performances of (1, λ)and (1 + 2)-Evolution-Strategies (ESs) with the aim of designing fa...
As computers become increasingly mobile, users demand more functionality, longer battery-life, and better performance from mobile devices. In response, chipset fabricators are foc...
James Kukunas, Robert D. Cupper, Gregory M. Kapfha...
We implement a weighted negative update of the covariance matrix in the CMA-ES—weighted active CMA-ES or, in short, aCMA-ES. We benchmark the IPOP-aCMA-ES and compare the perfor...
This paper argues that multiagent learning is a potential “killer application” for generative and developmental systems (GDS) because key challenges in learning to coordinate ...
The increase in the complexity of modern software has led to the commensurate growth in the size and execution time of the test suites for these programs. In order to address this...
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. The runtime ...
Nikolaus Hansen, Anne Auger, Raymond Ros, Steffen ...