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...
Automatic design of software architecture by use of genetic algorithms has already been shown to be feasible. A natural problem is to augment – if not replace – genetic algori...
The BFGS quasi-Newton method is benchmarked on the noisy BBOB-2009 testbed. A multistart strategy is applied with a maximum number of function evaluations of about 104 times the s...
This paper describes the application of a Gaussian Estimation-of-Distribution (EDA) for real-valued optimization to the noisy part of a benchmark introduced in 2009 called BBOB (B...
Evolution of neural networks, as implemented in NEAT, has proven itself successful on a variety of low-level control problems such as pole balancing and vehicle control. Nonethele...
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...
The restarted line search, or coordinate-wise search, algorithm is tested on the BBOB 2009 testbed. Two different univariate search algorithms (fminbnd from MATLAB and STEP) were...
In this paper we extend the work done in [5], where authors have proposed a evolutionary multi-objective approach to Rapid Prototyping (RP), to decipher optimal build orientation ...