No Optimization?

I was having a weird problem where my thing was not able to optimize. It was so weird. For high cdf values, it just couldn't improve the solution. I think after toying around with it today, I figured it out. when you increase the cdf value past a certain point, and the items were made to be 3 per bin, then the items have to be 2 per bin because they're all too big for the 3. So, you get a point where you can't optimize.

Pack Seperately

The pack seperately idea did not do as well as we had hoped for one test that I ran.

Stuff I'm getting done today

I redid some of the RGGA paper. I don't feel like it's in full form, but I put content in there.


The only thing that has changed is the introduction, the last part of the results and the conclusions.

Things to do

1) Gecco Paper
a) Introduction
b) Return to power & show actual power used
c) Time to run
2) Pack tight things Seperate
3) Start thinking about adding distributions and integrating that into the fitness function.

more merging

Here's one little quantile above the last graph.



Here's one little quantile less than the last graph.

A merged graph

Here's a graph kind of in the middle.

A merged graph

Here's a graph kind of in the middle.

A better graph.

I produced this.



I'd like to know why my graphs are all so different...

Yet another graph.

This is a graph of a two-resource problem with most servers being able to fit three vms.




The nice thing about this graph is that for very small values of percent of servers over capacity, Evolve and RepackEvolve both do very well. RepackEvolve does not perform much better than Evolve.

Another graph

This is another data set. As we can see, repacking offers a few benefits, but it's not as nice as the other graph.




This graph is nice, but not as nice as the one posted the other day.

Hitting a High Fittness on My Evolutionary Programming Approach

So, today, after doing some twiddling and tweaking, I hit a point on my evolutionary fitness with my program that I'm rather happy with.



This was done on a different data set with a different variance. There was quite a bit different for these results than for the results I published in the paper. However, if these results are good, then there are some data sets for which Evolve does much better for some percent of solutions infeasible.