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 ....
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 f...
We benchmark the IPOP-CMA-ES on the noisy testbed of the BBOB 2010 workshop. The performances of the IPOPCMA-ES are compared to those of the BIPOP-CMA-ES. Both algorithms are show...
This paper benchmarks the Artificial Bee Colony (ABC) algorithm using the noise-free BBOB 2010 testbed. The results show how this algorithm is highly successful in the separable a...
man and Kenneth O. Stanley (2010). Revising the Evolutionary Computation Abstraction: Minimal Criteria Novelty Search. In: Proceedings of the Genetic and Evolutionary Computation C...