Abstract
A cultural algorithm is proposed in this paper. The main novel feature of this approach is the use of differential evolution as a population space. Differential evolution has been found to be very effective when dealing with real valued optimization problems. The knowledge sources contained in the belief space of the cultural algorithm are specifically designed according to the differential evolution population. Furthermore, we introduce an influence function that selects the source of knowledge to apply the evolutionary operators. Such influence function considerably improves the performance when compared to a previous version of the algorithm (developed by the same authors). We use a well-known set of test functions to validate the approach, and compare the results with respect to the best constraint-handling technique known to date in evolutionary optimization.
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Becerra, R.L., Coello, C.A.C. (2004). A Cultural Algorithm with Differential Evolution to Solve Constrained Optimization Problems. In: Lemaître, C., Reyes, C.A., González, J.A. (eds) Advances in Artificial Intelligence – IBERAMIA 2004. IBERAMIA 2004. Lecture Notes in Computer Science(), vol 3315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30498-2_88
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DOI: https://doi.org/10.1007/978-3-540-30498-2_88
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