1. ECE 556 Project Algorithm Presentation
Saga - The Unified Placement Algorithm
Matt Kumerow, Phillip Heyrman, and Todd Tisch

2. Genotype vs Phenotype

3. Evaluate()
BestOf()
Crossover()
Mutation
Optimize

4. Evaluate() Computes the fitness of each individual in the population
Fitness is defined as the estimation of the total interconnect length, which is computed using the bounding box approach

5. BestOf()
Finds the individual in the population with the highest fitness
The individual is determined by comparing the fitness of each individual in the population as determined by a previous call to evaluate()

6. Reduce(Population, k)
Returns the k fittest individuals from the given population
Determined by calling evaluate() and selecting the k most fit individuals in the result set

7. Crossover()
Given two individuals the crossover operator generates an offspring
Uses a subtree of the first parent
Uses priority ordering of the second parent

8. Mutation Used to generate a random change of the phenotype
Two nodes exchanged
Leaf node can be moved to another position
Two priorities can be exchanged

9. SAMutate()
All phenotypes of a population are subjected to pointwise mutation
The mutation is performed with a temperature-dependent probability
Once a fixed amount of cost increase mutations are accepted, the temperature for that individual is reduced

10. Invert()
Selects a random subtree from genotype and alters it so that the genotype is different, yet it results in the same phenotype

11. Optimize Exhaustive strategy used to get local hill climbing
Performs a sequence of mutations, each which improves the fitness
Stops when we obtain a set number of accepted mutations

12. References
Genetic Algorithms for VLSI Design, Layout & Test Automation by Pinaki Mazumder, Elizabeth M. Rudnick
“SAGA: Unification of genetic algorithm with simulated annealing and its application to macro-cell placement” by H. Esbensen, P. Mazumder