# Category Archives: CodeProject

## How to run code daily at specific time in C# Part 2

Few months ago I wrote blog post about how to run code daily at specific time. I dint know that the post will be the most viewed post on my blog. Also there were several questions how to implement complete example. So today I have decided to write another post, and extend my previous post in order to answer thise question as well as to generalize this subject in to cool demo called Scheduler DEMO.

The post is presenting simple Windows Forms application which calls a method for every minute, day, week, month or year. Also demo shows how to cancel the scheduler at any time.

The picture above shows simple Windows Forms application with two  numeric control which you can set starting hour and minute for the scheduler. Next there is a button Start to activate timer for running code, as well as Cancel button to cancel the scheduler. When the time is come application writes the message on the Scheduler Log.

## Implementation of the scheduler

Scheduler is started by clicking the Start button which is implemented with the following code:

```/// <summary>
/// Setting up time for running the code
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private void startBtn_Click(object sender, EventArgs e)
{

//retrieve hour and minute from the form
int hour = (int)numHours.Value;
int minutes = (int)numMins.Value;

//create next date which we need in order to run the code
var dateNow = DateTime.Now;
var date = new DateTime(dateNow.Year, dateNow.Month, dateNow.Day, hour, minutes, 0);

//get nex date the code need to run
var nextDateValue=getNextDate(date,getScheduler());

runCodeAt(nextDateValue, getScheduler());

}
```

When the time is defined then the runCodeAt method is called which implementation can be like the following;

```/// <summary>
/// Determine the timeSpan Dalay must wait before run the code
/// </summary>
/// <param name="date"></param>
/// <param name="scheduler"></param>
private void runCodeAt(DateTime date,Scheduler scheduler )
{
m_ctSource = new CancellationTokenSource();

var dateNow = DateTime.Now;
TimeSpan ts;
if (date > dateNow)
ts = date - dateNow;
else
{
date = getNextDate(date, scheduler);
ts = date - dateNow;
}

//enable the progressbar
prepareControlForStart();

//waits certan time and run the code, in meantime you can cancel the task at anty time
{
//run the code at the time
methodToCall(date);

//setup call next day
runCodeAt(getNextDate(date, scheduler), scheduler);

},m_ctSource.Token);
}
```

The method above creates the cancelationToken needed for cancel the scheduler, calculate timeSpan – total waiting time, then when the time is come call the method methodToCall and calculate the next time for running the scheduler.

This demo also shows how to wait certain amount of time without blocking the UI thread.

The full demo code can be found on OneDrive.

## New Features in C# 6.0 – Auto-Property Initializers

Initialize property is repetitive task, and cannot be done in the same line as we can can done for fields. For example we can write:

```
public class Person
{
private string m_Name="Default Name";
public string Name {get;set;}
public Person()
{
Name=m_Name;
}

}
```

As we can see Property can be initialized only in the constructor, beside the filed which can be initialized in the same line where it is declared. The new feature in C# 6.0 defines Auto-Property initializer alowing property to be initialized like fields. The following code snippet shows the Auto-Property Initializer;

```
public class Person
{
static string m_Name="Default Name";
static string Name {get;set;}=m_Name;
}
```

## New Features in C# 6.0 – Null-Conditional Operator

This is blog post series about new features coming in the next version of C# 6.0. The first post is about null conditional operator.

The NullReferenceException is night mare for any developer specially for developer with not much experience. Almost every created object must be check against null value before we call some of its member. For example assume we have the following code sample:

```class Record
{
public Person Person  {get;set;}
public Activity Activity  {get;set;}
}
public static PrintReport(Record rec)
{
string str="";
if(rec!=null && rec.Person!=null && rec.Activity!=null)
{
str= string.Format("Record for {0} {1} took {2} sec.", rec.Person.FirstName??"",rec.Person.SurName??"", rec.Activity.Duration);
Console.WriteLine(str);
}

return ;
}
```

We have to be sure that all of the object are nonnull, otherwize we get NullReferenceException.

The next version of C# provides Null-Conditional operation which reduce the code significantly.

So, in the next version of C# we can write Print method like the following without fear of NullReferenceException.

```
public static PrintReport(Record rec)
{
var str= string.Format("Record for {0} {1} took {2} sec.", rec?.Person?.FirstName??"",rec?.Person?.SurName??"", rec?.Activity?.Duration);
Console.WriteLine(str);

return;
}
```

As we can see that ‘?’ is very handy way to reduce our number of if statements in the code. The Null-Conditional operation is more interesting when is used in combination of ?? null operator. For example:

```
string name=records?[0].Person?.Name??"n/a";

```

The code listing above checks if the array of records not empty or null, then checks if the Person object is not null. At the end null operator (??) in case of null value of the Name property member of the Person object put default string “n/a”.

For this operation regularly we need to check several expressions agains null value.
Happy programming.

## Function optimization with Genetic Algorithm by using GPdotNET

Content

1. Introduction
2. Analytic function optimization module in GPdotNET
3. Examples of function optimizations
4. C# Implementation behind GPdotNET Optimization module

Introduction

GPdotNET is artificial intelligence tool for applying Genetic Programming and Genetic Algorithm in modeling and optimization of various engineering problems. It is .NET (Mono) application written in C# programming language which can run on both Windows and Linux based OS, or any OS which can run Mono framework. On the other hand GPdotNET is very easy to use. Even if you have no deep knowledge of GP and GA, you can apply those methods in finding solution. The project can be used in modeling any kind of engineering process, which can be described with discrete data. It is also be used in education during teaching students about evolutionary methods, mainly GP and GA. GPdotNET is open source project hosted at http://gpdotnet.codeplex.com

With releasing of  GPdotNET v2 it is also possible to find optimum for any analytic function regardless of independent variables. For example you can find optimum value for an analytically defined function with 2, 5, 10 or 100 independent variables. By using classic methods, function optimization of 3 or more independent variables is very difficult and sometimes impossible. It is also very hard to find optimum value for functions which are relatively complex regardless of number of independent variables.
Because GPdotNET is based on Genetic Algorithm we can find approximated optimum value of any function regardless of the limitation of number of independent variables, or complex definition. This blog post is going to give you a detailed and full description how to use GPdotNET to optimize function. Blog post will also cover C# implementation behind optimization proce by showing representation of Chromosome with real number, as well as Fitness calculation which is based on Genetic Programming tree expression. In this blog post it will also be presented several real world problem of optimization which will be solved with GPdotNET.

# Analitic Function Optimization Module in GPdotNET

When GPdotNET is opened you can choose several predefined and calucalted models from various domain problems, as weel as creating New model among other options. By choosing New model new dialog box appears like picture below.

By choosing Optimization of Analytic Function (see pic above) and pressing OK button, GPdotNET prepares model for optimization and opens 3 tab pages:

1. Analytic function,
2. Settings and
3. Optimize Model.

## Analytic function

By using “Analytic function” tab you can define expression of a function. More information about how to define mathematics expression of analytic function can be found on this blog post.

By using “Analytic definition tool” at the bottom of the page, it is possible to define analytic expression. Expression tree builder generates function in Genetic Programming Expression tree, because GPdotNET fully implements both methods. Sharing features of Genetic programming  in Optimization based Genetic Algorithm is unique and it is implement only in GPdotNET.

When the process of defining function is finished, press Finish button in order to proceed with further actions. Finish button action apply all changes with Optimization Model Tab. So if you have made some changed in function definition, by pressing Finish button changes will be send to optimization tab.
Defining expression of function is relatively simple, but it is still not natural way for defining function, and will be changed in the future. For example on picture 2, you can see Expression tree which represent:

$f(x,y)=y sin{4x}+1.1 x sin{2y}$.

## Setting GA parameters

The second step in optimization is setting Genetic Algorithm parameter which will be used in optimization process. Open the Setting tab and set the main GA parameters, see pic. 3.

To successfully applied GA in the Optimization, it is necessary to define:

1.  population size,
2. probabilities of genetic operators and
3. selection methods.

These parameters are general for all GA and GP models. More information about parameters you can find at http://bhrnjica.net/gpdotnet.

## Optimize model (running optimization)

When GA parameters are defined, we can start with optimization by selecting Optimization model tab. Before run, we have to define constrains for each independent variables. This is only limitation we have to define i  order to start optimization. The picture below shows how to define constrains in 3 steps:

1.  select row by left mouse click,
2. enter min and max value in text boxes
3. Press update button.

Perform these 3 actions for each independent variable defined in the function.

When the process of defining constrains is finished, it is time to run the calculation by pressing Optimize button, from the main toolbar(green button). During optimization process GPdotNET is presenting nice animation of fitness values, as well as showing current best optimal value. The picture above shows the result of optimization process with GPdotNET. It can be seen that the optimal value for this sample is $f_{opt}(9.96)=-100.22$.

# Examples of function optimization

In this topic we are going to calculate optimal value for some functions by using GPdotNET. Zo be prove that the optimal value is correct or very close to correct value we will use Wolfram Alpha or other method.

### Function: x sin(4x)+1.1 x sin(2y)

GP Expression tree looks like the following picture (left size):

Optimal value is found (right above picture) for 0.054 min, at 363 generation of total of 500 generation. Optimal value is f(8.66,9.03)=-18.59.

Here is Wolfram Alpha calculation of the same function. http://www.wolframalpha.com/input/?i=min+x*sin%284*x%29%2B+1.1+*y*+sin%282+*y%29%2C+0%3Cx%3C10%2C0%3Cy%3C10

### Function:  (x^2+x)cos(x),  -10≤x≤10

GP expression tree looks like the following picture (left size):

Optimal value is found for 0.125 min, at 10 generation of total of 500 generation. Optimal value is F(9.62)=-100.22.

Here is Wolfram Alpha calculation of the same function. http://www.wolframalpha.com/input/?i=minimum+%28x%5E2%2Bx%29*cos%28x%29+over+%5B-10%2C10%5D

### Easom’s function fEaso(x1,x2)=-cos(x1)•cos(x2)•exp(-((x1-pi)^2+(x2-pi)^2)), -100<=x(i)<=100, i=1:2.

GP expression tree looks like the following picture (left size):

Optimal value is found for 0.061 min, at 477 generation of total of 500 generation. Optimal value is F(9.62)=-1, for x=y=3.14.

Function can be seen at this MatLab link.

# C# Implementation behind GPdotNET Optimization module

GPdotNET Optimization module is just a part which is incorporated in to GPdotNET Engine. Specific implementation for this module is Chromosome implementation, as well as Fitness function. Chromosome implementation is based on  floating point value instead of classic binary representation. Such a Chromosome contains array of floating point values and each element array represent function independent variable. If the function contains two independent variables (x,y) chromosome implementation will contains array with two floating points. Constrains of chromosome values represent constrains we defined during settings of the optimization process. The following source code listing shows implementation of GANumChrosomome class in GPdotNET:

```public class GANumChromosome: IChromosome
{
private double[] val = null;
private float fitness = float.MinValue;
//... rest of implementation
}
```

When the chromosome is generated array elements get values randomly generated between min and max value defined by function definition. Here is a source code of Generate method.

```///
/// Generate values for each represented variable
///
public void Generate(int param = 0)
{
if(val==null)
val = new double[functionSet.GetNumVariables()];
else if (val.Length != functionSet.GetNumVariables())
val = new double[functionSet.GetNumVariables()];

for (int i = 0; i < functionSet.GetNumVariables(); i++)

}
```

Mutation is accomplish when randomly chosen array element randomly change itc value. Here is a listing:

```///
///  Select array element randomly and randomly change itc value
///
public void Mutate()
{
//randomly select array element
//randomly generate value for the selected element
}
```

Crossover is little bit complicated. It is implemented based on Book Practical Genetic Algoritms see pages 56,57,48,59. Here is an implementation:

```///
/// Generate number between 0-1.
/// For each element array of two chromosomes exchange value based on random number.
///
///
public void Crossover(IChromosome ch2)
{
GANumChromosome p = (GANumChromosome)ch2;
double beta;
for (int i = crossoverPoint; i < functionSet.GetNumVariables(); i++)
{
val[i] = val[i] - beta * (val[i] - p.val[i]);
p.val[i] = p.val[i] + beta * (val[i] - p.val[i]);
}
}
```

Fitness function for Optimization is straightforward, it evaluates each chromosome against tree expression. For minimum the better chromosome is lower value. For maximum better chromosome is the chromosome with higher fitness value. Here is a implementation of Optimizatio Fitness function:

```///
/// Evaluates function agains terminals
///
///
///
///
public float Evaluate(IChromosome chromosome, IFunctionSet functionSet)
{
GANumChromosome ch = chromosome as GANumChromosome;
if (ch == null)
return 0;
else
{
//prepare terminals
var term = Globals.gpterminals.SingleTrainingData;
for (int i = 0; i < ch.val.Length; i++)
term[i] = ch.val[i];

var y = functionSet.Evaluate(_funToOptimize, -1);

if (double.IsNaN(y) || double.IsInfinity(y))
y = float.NaN;

//Save output in to output variable
term[term.Length - 1] = y;

if (IsMinimize)
y *= -1;

return (float)y;
}
}
```

# Summary

We have seen that Function optimization module within GPdotNET is powerful optimization tool. It can find pretty close solution for very complex functions regardless of number of independent variables. Optimization module use Genetic Algorithm method with floating point value chromosome representation described in several books about GA. It is fast, simple and can be used in education as well as in solving real problems. More info about GPdotNET can be found at http://bhrnjica.net/gpdotnet.