Evolutionary Computation

Due: Thursday, 9 November 2017 at the beginning of class

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

- Read
- Chapter 10 in textbook.

- Problems
- Take a numerical optimisation problem and a GA that is suited to solve it, i.e., uses the appropriate representation. (You may use any code to complete this assignment. Just note from where you got the code.) Select 3 different values for each of the parameters population size μ, mutation rate pm, and crossover rate pc . Execute 30 runs with each of the 27 different GA instances and for each run save the best fitness at termination, the number of fitness evaluations and the CPU time needed to complete the run. Perform a simple statistical analysis on the spread of the outcomes, e.g., calculate the minimum, the maximum, the average, the standard deviation, etc. Use all 27 setups as the basis of your statistics first, then fix one parameter at one of its values and do the same analysis for the 9 corresponding runs. How does this change your results? Summarise your observations.
- It could be argued that there is no survivor selection (replacement) step in GAVaPS, Discuss this issue.
- Why is it not possible to have self-adaptation operating at the population level?
- Provide the derivation for Equation 9.1 which gives the number of independent runs needed to find a solution by generation i with a probability of z.