Evolutionary Computation

Due: Tuesday, 10 October 2017 at the beginning of class

- Follow the general homework directions.
- Make sure you cite all your references and contacts.

- Read
- Chapter 6 and 7 in textbook.

- Problems
- Implement a simple genetic algorithm (SGA) in Matlab using Goldberg's pseudo code (posted on D2L under content). You must write the SGA from scratch. Investigate the SGA optimization capabilities on finding the minimizer of the Rosenbrock 2D landscape with a = 1 and b = 100. Experiment with the population size, mutation rate, and type of crossover. Run at least 10 experiments. Determine the average (over 20 runs) take over time, number of generations, and clock time to find the optimzier. Make a graphs of the parameter sets to show how varying the parameters effects the average number number of generations. Make sure to turn in any code needed to run the investigations.
- Now chose another fitness landscape. See how the best parameter sets for the Rosenbrock 2D landscape work on the new landscape. Repeat the same set of investigations on the new landscape. What conclusions can you draw from your investigations?
- Given the fitness function f(x) = x
^{2}, calculate selection probabilities for Fitness Proportional Selection for the individuals x=1, x=2, x=3. For the same individuals, calculate the selection probabilities for a transposed fitness function f'(x) = f(x) + 100.