Abstract
An open question that arises in the design of adaptive schemes for Dynamic Optimization Problems consists on deciding what to do with the knowledge acquired once a change in the environment is detected: forget it or use it in subsequent changes? In this work, the knowledge is associated with the selection probability of two local search operators in the Adaptive Hill Climbing Memetic Algorithm. When a problem change is detected, those probability values can be restarted or maintained. The experiments performed over five binary coded problems (for a total of 140 different scenarios) clearly show that kee** the information is better than forgetting it.
This work is supported in part by Projects TIN2011-27696-C02-01, Spanish Ministry of Economy and Competitiveness and P11-TIC-8001 from the Andalusian Government (including FEDER funds from the European Union).
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Calderín, J.F., Masegosa, A.D., Suárez, A.R., Pelta, D.A. (2014). Adaptation Schemes and Dynamic Optimization Problems: A Basic Study on the Adaptive Hill Climbing Memetic Algorithm. In: Terrazas, G., Otero, F., Masegosa, A. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2013). Studies in Computational Intelligence, vol 512. Springer, Cham. https://doi.org/10.1007/978-3-319-01692-4_7
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