| Resumen |
This paper describes a novel algorithm for numerical op- timization, which we call Simple Adaptive Climbing (
SAC). SAC is a simple efficient single-point approach that does not require a careful fine-tunning of its two parameters. Our algorithm has a close resemblance to local optimization heuristics such as random walk, gradient descent and,
hill-climbing. However, SAC algorithm is capable of performing global optimization efficiently in any kind of space. Tested on 15 well-known un- constrained optimization problems, it confirmed that SAC is competitive against representative state-of-the-art approaches |
