HW # 1
Part 1 (Due on March 1)

Using the function provided in Unit # 2, 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.

Part # 2 (Due on March 15)
Compare the two combinations of your choice (found in Part 1) against canonical versions of Evolutionary Programming and Evolutionary Strategies. For Evolutionary Strategies, use both (1 + lambda) and (mu + lambda) version.

Keep the population size as 10 for all the algorithms (except for 1+lambda ES).

For this part, use the Rosenbrock function provided in Unit # 3.

Part # 3 (Due on March 22)

Solve TSP using the following algorithms:

(i) Ant Colony Optimization
(ii) An Evolutionary Algorithm with "generational scheme"
(iii) An Evolutionary Algorithm with your favorite combination of parent/survival selection

Use the Qatar and Uruguay data sets available on the following link to test your algorithms
http://www.tsp.gatech.edu/ world/countries.html

Discuss your findings in the class

HW # 2 (Due on April 15)

Present a topic related to Computational Intelligence that is not covered in the given syllabus.



HW # 3

Implementation of backpropagation algorithm and a hybrid EA/PSO-NN.

Project

Presentation + Implementation