Meta-optimization

Meta-optimization is reported to have been used as early as in the late 1970s by Mercer and Sampson[1] 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.

Selecting the behavioural parameters by hand is a laborious task that is susceptible to human misconceptions of what makes the optimizer perform well.

[4] Meta-optimization of the COMPLEX-RF algorithm was done by Krus and Andersson,[5] and,[6] where performance index of optimization based on information theory was introduced and further developed.

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,[13] and Nannen and Eiben.