Abstract
There are presently many and seemingly different optimization algorithms, based on unrelated paradigms. Although some nice and important intuitions support those heuristics, there is (to our knowledge) no rigorous and systematic approach on how to relate them. Herein we present a framework to encompass those heuristics, based on the multiset formalism, providing a common working structure and a basis for their comparison. We show how to express some well known heuristics in our framework and we present some results on relations among them.
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Correia, L., Moura-Pires, F., Aparício, J.N. (1999). Expressing Population Based Optimization Heuristics Using PLATO. In: Barahona, P., Alferes, J.J. (eds) Progress in Artificial Intelligence. EPIA 1999. Lecture Notes in Computer Science(), vol 1695. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48159-1_26
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DOI: https://doi.org/10.1007/3-540-48159-1_26
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