Genetic Algorithm Assignment

Parameter representations for a Simple Genetic Algorithm (SGA)

Design and implement a simple genetic algorithm. A simple genetic algorithm uses fitness proportional selection, one-point crossover, and point mutation. Practice function optimization and binary multi-point, fixed-point representations.

Use this simple genetic algorithm to solve the first four DeJong functions. They are listed on page 108 in the textbook.

I would like to see a graphs of performance (fitness vs time) for each of the functions averaged over at least 30 runs. These graphs should show the average of the maximum fitness in the population for every generation, the average of the population average fitness for every generation, and the average of the minimum fitness in the population for every generation.

I would like to see at least nine nice graphs for every De-Jong function. All possible combinations of low values for the probabilities of crossover (0.2) and mutation (0.0001), nominal values for the probabilities of crossover (0.67) and mutation (0.001), and high values for the probabilities of crossover (0.99) and mutation (0.01).

Ensure that:

You stay below the limit of 5000 function evaluations.

Put these graphs on your web page and send me the URL.

Good Luck Let me know if you need more disk space.

Sushil Louis

Last modified: Wed Feb 23 14:16:10 PST 2005