This paper describes the implementation and the results for CMA-EGS on the BBOB 2010 noiseless function testbed. The CMA-EGS is a hybrid strategy which combines elements from grad...
Genetic algorithms—a class of stochastic population-based optimization techniques—have been widely realized as the effective tools to solve complicated optimization problems ...
As experimentation becomes one of the de-facto approaches for benchmarking, researchers are turning to testbeds to test, review and verify their work. As a result, several research...
Originally, genetic algorithms were developed based on the binary representation of candidate solutions in which each conjectured solution is a fixed-length string of binary numb...
We benchmark the Covariance Matrix Adaptation-Evolution Strategy (CMA-ES) algorithm with an Increasing POPulation size (IPOP) restart policy on the BBOB noiseless testbed. The IPO...