A Study of
Genetic Algorithms for
Parameter Optimization
Mac Newbold
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
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
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1 boolean, 4 integers, 15 floating point
 1 integer and 3 floats are scaling factors
 15 parameters need to be optimized
Genetic Algorithm
 Evolution, “survival of the fittest”
 Genetic Algorithm control – “tune”
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Calls object methods
 Replaceable object
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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
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
Fitness Function
 For “assign”, we care about running time

Choice of constants has huge effect
 Fitness calculation:
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Run “assign” on a set of N problems, using the
object’s parameters
• Allow S seconds for each run
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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
G.A. Results
 Tested genetic algorithm with “random”
objects
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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
Results
1000
800
600
Average
400
Max (999.X)
200
0
Init Gen1 Gen2 Gen3 Gen4 Gen5 Gen6 Gen7
What’s Next
 Finish setting up actual scoring using
“assign” runtimes…