1. Trivial Parallelization of an Existing EA Asher Freese CS401

2. Motivation Many difficult problems Many difficult problems –NP-Complete 0/1 Knapsack 0/1 Knapsack Traveling Salesman Traveling Salesman Initial Solution: Evolutionary Algorithm Initial Solution: Evolutionary Algorithm Problem: Problem: –EAs rarely get optimal solution –Some problems are even bigger than an EA can handle in reasonable time

3. Solution Parallel Computing (Island Model) Parallel Computing (Island Model) –Parallelization increases speed of programs –EAs are inherently parallelizable Easy to split populations Easy to split populations Most suitable uses Most suitable uses –Requires many generations to converge –Expensive fitness function

4. Previous Research Very Little Very Little –Proof of concept –Speed up computation of unrelated problem –Method of achieving population diversity

5. Approach Parallel, Island Model framework Parallel, Island Model framework –Parallel framework takes EA class Instantiation by main will not change Instantiation by main will not change –Loop Run for some generations Run for some generations Trade individuals among processes Trade individuals among processes –EA class inherits parent for parallel framework

6. Parallel EA Structure

7. Two types of Parallelization Purely Parellel Purely Parellel –Each EA is completely independent Island Model trading Island Model trading –EA’s mostly independent –Trade individuals occasionally

8. Testing N-queens problem N-queens problem 0/1 Knapsack (later) 0/1 Knapsack (later) Ackley Function (later) Ackley Function (later)

9. Results

10. Parallel Comparisons

11. Results, cont

12. Preliminary Conclusion Low epoch length, bad Low epoch length, bad Low number traded individuals, good Low number traded individuals, good High number of processors, good High number of processors, good

13. Future Work More tests More tests –0/1 Knapsack –Ackley Function Different trading algorithms Different trading algorithms Processor niching Processor niching

14. Questions?