Abstract
In complex engineering problems, often the objective functions can be very slow to evaluate. This paper introduces a new algorithm that aims to provide controllable exploration and exploitation of the decision space with a very limited number of function evaluations. The paper compares the performance of the algorithm to a typical evolutionary approach.
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© 2003 Springer-Verlag Berlin Heidelberg
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Hughes, E.J. (2003). Multi-objective Binary Search Optimisation. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds) Evolutionary Multi-Criterion Optimization. EMO 2003. Lecture Notes in Computer Science, vol 2632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36970-8_8
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DOI: https://doi.org/10.1007/3-540-36970-8_8
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