|
|
| Line 1: |
Line 1: |
| - | == Problem descriptions ==
| |
| | | | |
| - | Add your problem description here for the discussion in the statistics reading club of 23 Feb
| |
| - |
| |
| - | ==== Wojtek ====
| |
| - |
| |
| - | [http://www.few.vu.nl/~wojtek/files/StatsSlides.pdf Hereby] a compilation of my slides that I was using during the first two meetings. Pay special attention to slides 19-21 - they contain tasks for some of you
| |
| - | (Robert-Jan, Willem, Rob, Evert, Vincent) and give an idea of what to expect on Monday 21st (at 14:00).
| |
| - |
| |
| - | ==== Selmar/Gusz ====
| |
| - | The dataset consists of 9 subsets of experimental data. In each subset the results are given of 100 runs of a specific EA on a specific problem-function.
| |
| - | There are three different EAs, and three different functions: Rastrigin, Sphere and a handcrafted stepped function.
| |
| - |
| |
| - | For each run the following metrics are saved:
| |
| - | * Best fitness in the final population
| |
| - | * The number of problem evaluations needed to find the solution
| |
| - | * If the run terminated succesfully
| |
| - |
| |
| - | The question is: Which EA is the best?
| |
| - |
| |
| - | Download the dataset [http://www.cs.vu.nl/~sksmit/SRC.zip HERE] (zipped .MAT files)
| |
| - |
| |
| - | ==== Martijn ====
| |
| - | [[Image:oanec-stats.png|thumb|example results]]
| |
| - |
| |
| - | We investigate effects of reciprocity and transivity in network formation. The dataset consists of 3 X 4 X 5 subsets over three dimensions:
| |
| - |
| |
| - | * '''frequency_of_informal_opportunities''' \in [LOW, MEDIUM, HIGH]
| |
| - | * '''[operational_transitivity, operational_reciprocity]''' \in [[no,no], [yes,no],[no,yes],[yes,yes]]
| |
| - | * '''specialisation''' \in [admin, electro, nautical, technical, marines]
| |
| - |
| |
| - | We measured number of reciprocated ties.
| |
| - |
| |
| - | The question is: are the observed differences significant?
| |
| - | Or: what dimension has largest impact on number of reciprocated ties?
| |
| - |
| |
| - | I have the dataset, but not readily available to include here.
| |
| - |
| |
| - | ==== Willem ====
| |
| - | This is a problem I had with a previous paper.
| |
| - |
| |
| - | Stripped down, my problem comes down to the following:
| |
| - | I have 3 algorithms: 2 benchmarks and 1 new algorithm. I want to show
| |
| - | that my new algorithm outperforms the other algorithms. The output of
| |
| - | the algorithms is a single number, the 'value'.
| |
| - |
| |
| - | The goal of the
| |
| - | algorithm is to locate (static) targets, who are distributed over some
| |
| - | terrain. I took 10 different target distributions, and tested each
| |
| - | algorithm on these distributions, with 10 different initial random
| |
| - | seeds.
| |
| - |
| |
| - | The problem here is that the random seed makes a lot of difference in
| |
| - | how well the algorithms perform. This means that the mean performance
| |
| - | of an algorithm over different random seeds doesn't give me much
| |
| - | information. But, I can compare the outcomes of different algorithms
| |
| - | using the same initial random seed.
| |
| - |
| |
| - | So, at each run, I computed the difference in value for new algorithm
| |
| - | vs. the two benchmarks. To show that my algorithm outperforms the
| |
| - | other algorithms, I now only have to show that these differences are
| |
| - | significantly higher than 0. I did this using the wilcoxon signed rank
| |
| - | test.
| |
| - |
| |
| - | Attached you will find 2 data sets and 2 plots. The first data file
| |
| - | contains the difference between the new algorithm and the first
| |
| - | benchmark, the second date file contains the difference between the
| |
| - | new algorithm and the second benchmark. (the original outcomes of the
| |
| - | algorithms are on a different computer than i'm on right now, so I
| |
| - | cannot send you these.) Each row in the .dat files are the results for
| |
| - | one target distribution.
| |
| - |
| |
| - | The two .pdf files are the plots for these differences. If you look at
| |
| - | them, you intuitively see that the the differences are generally
| |
| - | higher than 0. But, what is the best test to show this?
| |
| - |
| |
| - | [http://www.few.vu.nl/~willem/files/dumb_vs_smart.dat dumb_vs_smart.dat] (plain text)
| |
| - | [http://www.few.vu.nl/~willem/files/dumb_vs_smart.pdf dumb_vs_smart.pdf] (pdf)
| |
| - | [http://www.few.vu.nl/~willem/files/det_vs_smart.dat det_vs_smart.dat] (plain text)
| |
| - | [http://www.few.vu.nl/~willem/files/det_vs_smart.pdf det_vs_smart.pdf] (pdf)
| |
