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 = /1000
Took 7 generations, about 5 seconds

8. Results

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