dist_crowding

Takes a <code>matrix</code> of fitness values and calculates the crowding distance for individuals in that <code>matrix</code>.
Individuals that are minimal or maximal with respect to at least one dimension are assigned infinite
crowding distance.
Individuals are assumed to be in a (epsilon-) nondominated front.

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>.

Martin Binder

miesmuschel

Mixed Integer Evolution Strategies

Lennart Schneider

Susanne Dandl

Andreas Hofheinz

dist_crowding function

<dl><dt>fitnesses</dt>
<dd>(<code>numeric matrix</code>) 
fitness matrix, with one row per individual and one column per objective</dd></dl>

Arguments

Calculate Crowding Distance — dist_crowding

<dl>

<dt>fitnesses</dt>
<dd>(<code>numeric matrix</code>) 
fitness matrix, with one row per individual and one column per objective</dd>

</dl>

dist_crowding: Calculate Crowding Distance

Description

Usage

Value

Arguments