Assignments

**(Posted on Feb 6, 2012 - Discussion on Feb 13, 2012)**
 * Assignment # 0**

This assignment will not be graded now but, once successfully implemented, will be helpful when you implement Ant Colony Optimization algorithm.

You are required to implement a simple TSP program which takes a text file (containing a 8x8 distance matrix) and return a permutation (path) using the greedy algorithm discuss in the class. Feel free to pick any starting point (node) of your choice or you can iterate through all the nodes as starting point and return the best path.

**Assignment # 1 (a)** **(Posted on Feb 16, 2012 - Due: 2 AM on March 4, 2012 )** **Marks: 4**

Using the functions provided in Unit # 5, compare the performance of different combination of selection schemes in an EA. The following schemes should be considered: Parent Selection: Fitness Proportional, Rank-based and Binary Tournament Survival Selection: Truncation and Binary Tournament

Each combination should be run at least 10 times and the evaluation should be performed using: (i) average best-so-far curve and (ii) average average-population fitness curve

You will present your findings in the class. You are welcome to try your own ideas as well in addition to the tasks described above.

**Assignment # 1 (b)** **(Posted on Mar 5, 2012 - Due: 5 PM on March 22, 2012 )**
 * Marks: 4 **

Compare the two combinations of your choice (found in Part a) against canonical versions of Evolutionary Programming and Evolution Strategy. For Evolution Strategy, use (mu, lambda) version.

Keep the population size (mu) as 10 for all the algorithms. Lambda should be set to 15 for ES.

The functions used in this part are:

a) Rosenbrock function (Function # 2) of Part (a) b) Himmelblau's function [] where ranges of x and y are: -4 < x, y < 4

**Assignment # 1 (c)** **(Posted on Mar 9, 2012 - Due: 5 PM on March 29, 2012 )**
 * Marks: 4 **

Compare the two combinations of your choice (found in Parts a and b) against Particle Swarm Optimization (PSO).

Keep the population size (mu) as 10 for all the algorithms.

The functions used in this part are:

a) Rosenbrock function (Function # 2) of Part (a) b) Himmelblau's function of Part (b)

**Assignment # 1 (d)** **(Posted on Mar 9 - Due: 5 PM on April 9, 2012)**
 * Marks: 4 **

Compare the two combinations of your choice (found in Parts a and b) and Particle Swarm Optimization (PSO) (Part c) against Artificial Immune Systems.

Keep the population size (mu) as 10 for all the algorithms.

The functions used in this part are:

a) Rosenbrock function (Function # 2) of Part (a) b) Himmelblau's function of Part (b)

**Assignment # 2** **(Posted on Mar 9 - Due: 2 AM on April 16, 2012)**
 * Marks: 4 **

This is a group assignment (ideal group size is 3). You are required to present an application of an EC technique. I would recommend that you select a journal paper on EC and summarize its finding in the class.

**Assignment # 3** **(Posted on March 9 - Due: 5 PM on May 7, 2012**
 * Marks: 10 **

Train a Neural Network using the data set provided in the following file.

The data set represent the KSE Index values during 2004 to 2006. There are four columns in the file, namely, Index(t-3), Index(t-2), Index(t-1) and Index(t), represented as X1, X2, X3 and Y, respectively. Your job is to learn Index(t) as a function of (Index(t-1), Index(t-2) and Index(t-3)). In other words, your job is to predict the value of tomorrow's index value using the values of today, yesterday and day before yesterday.

The training of the neural network should be done in the following manner:

(i) Backpropagation (ii) Weights Evolution using either EA or PSO (Evolutionary Neural Network)

Plot the values of the original index(t), predicted index(t) using Backpropagation and Evolutionary Neural Network.

Also report the correlation between the actual and predicted values (of both approaches) and the time taken by both approaches.

Describe your findings.

**Project** **(Posted on March 9 - Due: 5 PM on May 21, 2012**
 * Marks: 15 **