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Luque-Polo, Gabriel Jesús Alba-Torres, Enrique 2013-07-15T10:12:44Z 2013-07-15T10:12:44Z 2013 G. Luque, E. Alba, Math Oracles: A New Way of Designing Efficient Self-Adaptive Algorithms, Proceedings of the Genetic and Evolutionary Computation Conference Companion,pp. 217-218, GECCO'13, July 6–10, 2013, Amsterdam, The Netherlands. ACM 2013, ISBN 978-1-4503-1964-5. http://hdl.handle.net/10630/5612 In this paper we present a new general methodology to develop self-adaptive methods at a low computational cost. Instead of going purely ad-hoc we de ne several simple steps to include theoretical models as additional information in our algorithm. Our idea is to incorporate the predictive information (future behavior) provided by well-known mathematical models or other prediction systems (the oracle) to build enhanced methods. We show the main steps which should be considered to include this new kind of information into any algorithm. In addition, we actually test the idea on a speci c algorithm, a genetic algorithm (GA). Experiments show that our proposal is able to obtain similar, or even better results when it is compared to the traditional algorithm. We also show the bene ts in terms of saving time and a lower complexity of parameter settings. eng open access Lenguajes de programación Computación evolutiva Algoritmos genéticos Math Oracles: A New Way of Designing Efficient Self-Adaptive Algorithms journal article