The NEWUOA which belongs to the class of Derivative-Free optimization algorithms is benchmarked on the BBOB-2009 noisefree testbed. A multistart strategy is applied with a maximum...
A partly time and space linear CMA-ES is benchmarked on the BBOB-2009 noisy function testbed. This algorithm with a multistart strategy with increasing population size solves 10 f...
We benchmark an independent-restart-(1+1)-CMA-ES on the BBOB-2009 noisy testbed. The (1+1)-CMA-ES is an adaptive stochastic algorithm for the optimization of objective functions d...
The (1+1)-ES with one-fifth success rule is one of the first and simplest stochastic algorithm proposed for optimization on a continuous search space in a black-box scenario. In...
We benchmark the BI-population CMA-ES on the BBOB2009 noisy functions testbed. BI-population refers to a multistart strategy with equal budgets for two interlaced restart strategi...