A Study of Genetic Algorithms for Parameter Optimization

1. A Study of Genetic Algorithms for Parameter Optimization Mac Newbold

2. Introduction • Many algorithms have constant values that affect the way they work • Sometimes we choose them arbitrarily or based on some experimentation • Their interactions are often not well understood • Use Genetic Algorithm to optimize the parameters to an algorithm

3. Background • Utah Network Testbed (www.emulab.net) • Map a “virtual” topology graph to the physical topology graph • NP-Hard, 30+ degrees of freedom • “assign” – Simulated Annealing (AI algo.) • 19 constants control behaviors • 1 boolean, 4 integers, 15 floating point • 1 integer and 3 floats are scaling factors • 15 parameters need to be optimized

4. Genetic Algorithm • Evolution, “survival of the fittest” • Genetic Algorithm control – “tune” • Calls object methods • Replaceable object • Obj->Random() – returns a random object • Obj->Fitness() – calculate fitness of object • Obj->Cross(obj2) – crossover (returns 2 objs) • Obj->Mutate() – mutate • Obj->Display() – Show the object • Very Flexible Framework

5. Parameter Optimization • “Params” object • Specialized for “assign” • Contains the 15 variables we want to tune • One extra value caches fitness calculations • Insures that values “make sense” using domain specific constraints • Uniform crossover • Random mutation • Performance based fitness measure

6. Fitness Function • For “assign”, we care about running time • Choice of constants has huge effect • Fitness calculation: • Run “assign” on a set of N problems, using the object’s parameters • Allow S seconds for each run • X=Sum of execution times • Fitness = (S*N) – X • S*N = maximum possible total time • Higher scores are better • Could take a long time, so cache result

7. G.A. Results • Tested genetic algorithm with “random” objects • Same as “Params” object, except for fitness • Random fitness, updated after cross/mutate • 1000 member population • Crossover rate of 0.50 • Mutation rate of 0.30 • Threshold = 999.995/1000 • Took 7 generations, about 5 seconds

8. Results

9. What’s Next • Finish setting up actual scoring using “assign” runtimes…