more cleanup 5

Author: thomasWeise

index.md

@@ -27,7 +27,7 @@ The {{ site.projectNameStyled }} framework prescribes the following work flow:
 1. *Algorithm Implementation:* You implement your algorithm. Do it in a way so that you can generate log files containing rows such as (`passed runtime`, `best solution quality so far`) for each run (execution) of your algorithm. You are free to use any programming language and run it in any environment you want. We don't care about that, we just want the text files you have generated.
 2. *Choose Benchmark Instances:* Choose a set of (well-known) problem instances to apply your algorithm to.
 3. *Experiments:* Well, run your algorithm, i.e., apply it a few times to each benchmark instance. You get the log files. Actually, you may want to do this several times with different parameter settings of your algorithm. Or maybe for different algorithms, so you have comparison data.
-4. *Use Evaluator:* Now, you can use our evaluator component to find our how good your method works! For this, you can define the *dimensions* you have measured (such as runtime and solution quality), the features of your benchmark instances (such as number of cities in a Traveling Salesman Problem or the scale and symmetry of a numerical problem), the parameter settings of your algorithm (such as population size of an EA), the information you want to get (ECDF? performance over time?), and how you want to get it (LaTeX, optimized for IEEE Transactions, ACM, or Springer LNCS? or maybe XHTML for the web?). Our evaluator will create the report with the desired information in the desired format.
+4. *Use Evaluator:* Now, you can use our evaluator component to find our how good your method works! For this, you can define the *dimensions* you have measured (such as runtime and solution quality), the features of your benchmark instances (such as number of cities in a [Traveling Salesman Problem](https://thomasweise.github.io/research/areas/tsp) or the scale and symmetry of a numerical problem), the parameter settings of your algorithm (such as population size of an EA), the information you want to get (ECDF? performance over time?), and how you want to get it (LaTeX, optimized for IEEE Transactions, ACM, or Springer LNCS? or maybe XHTML for the web?). Our evaluator will create the report with the desired information in the desired format.
 5. By interpreting the report and advanced statistics presented to you, you can get a deeper insight into your algorithm's performance as well as into the features and hardness of the benchmark instances you used. You can also directly use building blocks from the generated reports in your publications. 
 
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