Abstract
This paper describes a novel algorithm for numerical optimization, 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 unconstrained optimization problems, it confirmed that SAC is competitive against representative state-of-the-art approaches.
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Viveros-Jiménez, F., León-Borges, J.A., Cruz-Cortés, N. (2013). A Simple Adaptive Algorithm for Numerical Optimization. In: Batyrshin, I., Mendoza, M.G. (eds) Advances in Computational Intelligence. MICAI 2012. Lecture Notes in Computer Science(), vol 7630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37798-3_11
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DOI: https://doi.org/10.1007/978-3-642-37798-3_11
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