page_title: Meta-optimization
section_title: Methods
hierarchical_section_title: Meta-optimization / Methods
caption_attribution_description: English: Meta-optimization progress of finding good behavioural parameters NP and F of Differential Evolution (DE) with a fixed parameter CR = 0.9, when DE is used for optimizing several benchmark problems problems in aggregate. Lines show moves made by the meta-optimizer (the method Local Unimodal Sampling, or LUS, is being used as meta-optimizer), crosses show moves contemplated but not taken as they would lead to worse meta-fitness. These are superimposed on the meta-fitness or performance landscape to show the valley of good performing parameters is actually found.
context_page_description: In numerical optimization, meta-optimization is the use of one optimization method to tune another optimization method. Meta-optimization is reported to have been used as early as in the late 1970s by Mercer and Sampson for finding optimal parameter settings of a genetic algorithm.
  Meta-optimization and related concepts are also known in the literature as meta-evolution, super-optimization, automated parameter calibration, hyper-heuristics, etc.
context_section_description: A simple way of finding good behavioural parameters for an optimizer is to employ another overlaying optimizer, called the meta-optimizer. There are different ways of doing this depending on whether the behavioural parameters to be tuned are real-valued or discrete-valued, and depending on what performance measure is being used, etc.
  Meta-optimizing the parameters of a genetic algorithm was done by Grefenstette  and Keane, amongst others, and experiments with meta-optimizing both the parameters and the genetic operators were reported by Bäck. Meta-optimization of the COMPLEX-RF algorithm was done by Krus and Andersson, and, where performance index of optimization based on information theory was introduced and further developed. Meta-optimization of particle swarm optimization was done by Meissner et al., Pedersen and Chipperfield, and Mason et al. Pedersen and Chipperfield applied meta-optimization to differential evolution. Birattari et al. meta-optimized ant colony optimization. Statistical models have also been used to reveal more about the relationship between choices of behavioural parameters and optimization performance, see for example Francois and Lavergne, and Nannen and Eiben. A comparison of various meta-optimization techniques was done by Smit and Eiben.
caption: Meta-optimization of differential evolution.
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