Scalarizer

<code>Scalarizer</code> objects are functions taking a fitness-matrix <code>fitnesses</code> (Nindivs x Nobjectives, with higher values indicating higher desirability)
and a list of weight matrices <code>weights</code> (Nindivs elements of Nobjectives x Nweights matrices; positive weights indicate a positive contribution
to scale) and returns a matrix of scalarizations (Nindivs x Nweights, with higher values indicating greater desirability).
Any other function conforming to these requirements can also be used in place of a <code>Scalarizer</code>, but the provided <code>Scalarizer</code> functions cover
the most common use cases.
<code>Scalarizer</code>s are constructed from constructor-functions, such as <code>scalarizer_linear()</code> or <code>scalarizer_chebyshev()</code>.

Evolutionary black box optimization algorithms building on the
'bbotk' package. 'miesmuschel' offers both ready-to-use optimization
algorithms, as well as their fundamental building blocks that can be used to
manually construct specialized optimization loops. The Mixed Integer Evolution
Strategies as described by Li et al. (2013) <doi:10.1162/EVCO_a_00059> can be
implemented, as well as the multi-objective optimization algorithms NSGA-II
by Deb, Pratap, Agarwal, and Meyarivan (2002) <doi:10.1109/4235.996017>.

Scalarizer: Scalarizer

Description

Arguments

See Also