-
**Is your feature request related to a problem? Please describe.**
When use compare_model, I can use n_select to pick n number of models with high metric. Usually when ensemble even not top model wil…
-
-
👋 I was wondering if there's a reason that [Stochastic] Hill Climbing isn't part of `Optim.jl` – I definitely get that Simulated Annealing is a "better" version of [S]HC, but [S]HC is still useful for…
-
According to referenced "Clever Algorithms: Nature-Inspired Programming Recipes":
> neighbors with **better or equal** cost should be accepted, **allowing the technique to navigate across plateaus*…
-
:red_circle: **Title** : Add Simple hill climbing and steepest ascent algorithms in AI -> Algorithms
:red_circle: **Enhancement Aim** : Simple hill climbing and steepest ascent algorithms in AI -> Al…
-
Write a Python Program for this
-
*Issue migrated from trac ticket # 2811*
**milestone:** HeuristicLab 3.3.17 | **component:** Algorithms | **priority:** medium
#### 2017-07-17 17:25:01: @abeham created the issue
___
> Late accept…
-
If we don't try to find the comparators to use in configurations, hill climbing might work much faster than genetic algorithms.
Nicolas Maisonneuve suggests: "by only searching high , low parameters,…
-
Implement the runnable interface so that we can easily spinoff threads of hill climbing.
-
Figure 4.5 - SIMULATED-ANNEALING describes an optimization algorithm that is supposed to return a state, as per the first line of the function declaration. The rest of the implementation, however, goe…