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资源说明：Evolutionary Algorithms Rating System
NEW VERSION of the sistem is now on
https://github.com/UM-LPM/EARS
Evolutionary Algorithms Rating System
=====================================
EARS in action http://earatingsystem.appspot.com
What is included:
* some banchmarks with problem functions (Sphere, ...).
* some already implemented test Alorithms
* some simple test experiments
How to use it!
* All projects are Eclipse java projects.
* Download it use git in eclipse.
* In same workspace create new java project.
* Add Properties -> Java Build Path -> Projects -> EARS
* Include your algorithm in the project.
* Modify algorithm to work with EARS
Example:
```java
public class RandomWalkAlgorithm extends Algorithm { //needs to me extended
private Individual i; //EARS Individual includes solution vector and its fitness value
private boolean debug = true;
public RandomWalkAlgorithm() {
super();
setDebug(debug); //EARS prints some debug info
ai = new AlgorithmInfo("","","RWSi+","Random Walk+"); //EARS add algorithm name
au = new Author("robi", "N/A"); //EARS author info
}
@Override
public Individual run(Task taskProblem) throws StopCriteriaException{ //EARS main evaluation loop
Individual ii;
i = taskProblem.getRandomIndividual(); //EARS Helper for creating random solution, it takes one evaluation (eval++)
//user can use its own representation for example double[] and in fase of evaluation calls taskProblem.eval that creates individual
System.out.println(taskProblem.getNumberOfEvaluations()+" "+i); //prints number of evaluations
while (!taskProblem.isStopCriteria()) { //EARS user needs to take care about number of evaluations
ii = taskProblem.getRandomIndividual();
if (taskProblem.isFirstBetter(ii, i)) { //EARS primary function it takes care if we are searching minimum or maximum, if solution is valit etc.
i = ii;
if (debug) System.out.println(taskProblem.getNumberOfEvaluations()+" "+i);
}
}
return i;
}
@Override
public void resetDefaultsBeforNewRun() {
i=null;
}
}
```
Run it on single task:
- Run your algorithm.
Example:
```java
public class Main4Run {
public static void main(String[] args) {
Task t = new Task(EnumStopCriteria.EVALUATIONS, 3000, 0.0001, new ProblemSphere(5)); //run problem Sphere Dimension 5, 3000 evaluations
RandomWalkAlgorithm test = new RandomWalkAlgorithm();
try {
System.out.println(test.run(t)); //prints best result afrer 3000 runs
} catch (StopCriteriaException e) {
e.printStackTrace();
}
}
}
```
Compare:
* For rating you need more than one algorithm (player) and more than one task (banchmark)).
Example:
```java
public class MainBenchMarkTest {
public static void main(String[] args) {
Util.rnd.setSeed(System.currentTimeMillis());
RatingBenchmark.debugPrint = true; //prints one on one results
ArrayList players = new ArrayList();
players.add(new ES1p1sAlgorithm()); //EARS exampels
players.add(new TLBOAlgorithm()); //EARS examples
players.add(new RandomWalkAlgorithm());
ResultArena ra = new ResultArena(100);
RatingRPUOed2 suopm = new RatingRPUOed2(); //Create banchmark
for (Algorithm al:players) {
ra.addPlayer(al.getID(), 1500, 350, 0.06,0,0,0); //init rating 1500
suopm.registerAlgorithm(al);
}
BankOfResults ba = new BankOfResults();
suopm.run(ra, ba, 50); //repeat competition 50X
ArrayList list = new ArrayList();
list.addAll(ra.recalcRangs()); //new rangs
for (Player p: list) System.out.println(p); //print rangs
}
}
```
Tips
____
* If you have special representation create your own individual by extending EARS Individual.
"class MyIndividual extends Individual"
* Search for main methods in EARS source code for more examples.
* All problem data (Dimension, Bounds, etc...) can be obtaint by Task in method public Individual run(Task taskProblem).
* Check taskProblem.isStopCriteria() after every evaluation.
本源码包内暂不包含可直接显示的源代码文件，请下载源码包。
