Definition
Hybrid parameter optimization (HPO) methods are search strategies that combine the strengths of different optimization algorithms to increase the performance of the overall parameter search process.
Detailed Description
Parameter optimization tends to be very complex, especially for the situations it is used for in computational neuroscience. Due to the many nonlinearities in neuronal systems and the large number of free parameters, the fitness landscapes tend to be highly dimensional and non-convex (Prinz et al. 2003; Achard and De Schutter 2006; Druckmann et al. 2007). Every optimization algorithm is optimal for certain shapes and sizes of the solution space (Achard et al. 2010). Unfortunately, the shape can change depending on the resolution one scans the parameters. On a macroscopic scale, the problem might look very non-convex with a lot of local minima, but close-ups around the optimal solutions could show a much more convex local environment.
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References
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Van Geit, W. (2014). Hybrid Parameter Optimization Methods. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7320-6_164-1
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DOI: https://doi.org/10.1007/978-1-4614-7320-6_164-1
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