====== Assignment ======
Each student will work on an assignment. The assignment consists of:
  * report in English (preferred) or Czech: 20 points (min 10 points), guidelines for writing the report are listed below
  * implementation (any programming language is possible): 10 points,


Use exclusively the following templates for writing your report: 
  * {{:courses:a4m33bia:bia-word-template.doc|Word}}
  * {{:courses:a4m33bia:ieeetran.zip|Latex}}


Use [[https://cw.felk.cvut.cz/ulohy/|Upload system]] to submit the assignment. 

**Deadline for the program implementation is May 19, 11:00 am.**

**Deadline for the report submission is May 26, 11:00 am.**


**__Guidelines for writing the report__**

**General:**
  * Only the templates provided must be used. A report not using the template will not be accepted.
  * Pay attention to the clarity of the text so that it is properly structured and formatted.
  * Do not be unduly concise. Make sure that there is all information necessary to understand the text, especially the description of the proposed algorithms.
  * Report must start with a title specifying the solved problem and Author’s name.
  * Use references to tables, figures, literature.
  * As a technical report it must contain a list of References (the number of referred items is not important) at the end.

**Description of the algorithm has to cover:**
  * Description of a genetic representation of the solution
  * Definition of the fitness
  * Description of genetic operators, selection, construction heuristics, local optimization heuristics
  * Description of the evolutionary model, i.e., the process through which a new population is generated from the current one 

**For neural networks you have to cover:**
  * Description of ANN topologies and neuron types used
  * Description of a learning algorithm (if you use a well-know algorithm a short description will suffice)
  * Description of inputs/outputs
  * Description of error measure (for supervised tasks)

**Description of conducted experiments has to include:**
  * Setting of the control parameters of compared algorithms
  * Number of runs conducted for each experiment
  * Definition of observed performance criteria (average best, mean best, standard deviation, ...)
  * Presentation of the achieved results (in the form of tables and graphs)
  * Discussion of the observed algorithm performance

**Graphs should**
  * have all axes annotated
  * have a legend clearly distinguishing curves if there are more than one in a single plot
  * use proper scale so that the important trend in data is clearly presented

Extra points can be obtained for high quality English texts.