Abstract
Solving ill-posed continuous, global optimization problems remains challenging. For example, there are no well-established methods for handling objective insensitivity in the neighborhood of solutions, which appears in many important applications, e.g., in non-invasive tumor tissue diagnosis or geophysical exploration. The paper presents a complex metaheuristic that identifies regions of objective function’s insensitivity (plateaus). The strategy is composed of a multi-deme hierarchic memetic strategy coupled with random sample clustering, cluster integration, and special kind of multiwinner selection that allows to breed the demes and cover each plateau separately. We test the method on benchmarks with multiple non-convex plateaus and evaluate how well the plateaus are covered.
The work presented in this paper has been partially supported by National Science Centre, Poland grant no. 2015/17/B/ST6/01867 and by the AGH statutory research grant no. 11.11.230.124.
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Notes
- 1.
In the theory of elections it is often assumed that a rule can output several tied committees, and these ties have to somehow be broken. In our application it is far simpler to assume that tie-breaking already happened within the rule and we get a unique outcome.
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Sawicki, J., Smołka, M., Łoś, M., Schaefer, R., Faliszewski, P. (2017). Two-Phase Strategy Managing Insensitivity in Global Optimization. In: Squillero, G., Sim, K. (eds) Applications of Evolutionary Computation. EvoApplications 2017. Lecture Notes in Computer Science(), vol 10199. Springer, Cham. https://doi.org/10.1007/978-3-319-55849-3_18
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