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Genetic Algorithm revisited Genetic Algorithm Operators Concurrency Some Results Future Work Implementation Report: Concurrent Genetic Algorithm with Island Migration Markus Solbach Laboratory for Active and Attentive Vision Department of

  1. Genetic Algorithm revisited Genetic Algorithm Operators Concurrency Some Results Future Work Implementation Report: Concurrent Genetic Algorithm with Island Migration Markus Solbach Laboratory for Active and Attentive Vision Department of Computer Science and Engineering York University, Toronto, Ontario, Canada November 10, 2015 1/25 Markus Solbach York University Implementation Report: Concurrent Genetic Algorithm with Island Migration

  2. Genetic Algorithm revisited Genetic Algorithm Operators Concurrency Some Results Future Work Overview Genetic Algorithm revisited Genetic Algorithm Operators Concurrency Some Results Future Work Figure : Evolution � i.livescience.com (Oct. 5. 15) 2/25 Markus Solbach York University Implementation Report: Concurrent Genetic Algorithm with Island Migration

  3. Genetic Algorithm revisited Genetic Algorithm Operators Concurrency Some Results Future Work Genetic Algorithm revisited 3/25 Markus Solbach York University Implementation Report: Concurrent Genetic Algorithm with Island Migration

  4. Genetic Algorithm revisited Genetic Algorithm Operators Concurrency Some Results Future Work Genetic Algorithm revisited Generation 0 4/25 Markus Solbach York University Implementation Report: Concurrent Genetic Algorithm with Island Migration

  5. Genetic Algorithm revisited Genetic Algorithm Operators Concurrency Some Results Future Work Genetic Algorithm revisited Generation n 5/25 Markus Solbach York University Implementation Report: Concurrent Genetic Algorithm with Island Migration

  6. Genetic Algorithm revisited Genetic Algorithm Operators Concurrency Some Results Future Work VLSI Design Problem Figure : VLSI Design Problem 6/25 Markus Solbach York University Implementation Report: Concurrent Genetic Algorithm with Island Migration

  7. Genetic Algorithm revisited Genetic Algorithm Operators Concurrency Some Results Future Work VLSI Design Problem - Traveling Salesman Problem Problem changed for good reasons: VLSI very little implementation details VLSI faced problems had nothing to do with concurrency TSP well known problem TSP rich implementation details in literature (sequential) TSP able to concentrade more on concurrency 7/25 Markus Solbach York University Implementation Report: Concurrent Genetic Algorithm with Island Migration

  8. Genetic Algorithm revisited Genetic Algorithm Operators Concurrency Some Results Future Work Traveling Salesman Problem Find a route on a map Requirements: ◮ Visit each City only once ◮ Find shortest path Complexity (20 city): ◮ O(n!) (Brute Force, WC) ◮ 20! = 2 . 432902 x 10 18 Figure : TSP Map 8/25 Markus Solbach York University Implementation Report: Concurrent Genetic Algorithm with Island Migration

  9. Genetic Algorithm revisited Genetic Algorithm Operators Concurrency Some Results Future Work Genetic Algorithm Operators 9/25 Markus Solbach York University Implementation Report: Concurrent Genetic Algorithm with Island Migration

  10. Genetic Algorithm revisited Genetic Algorithm Operators Concurrency Some Results Future Work Initialization Figure : High Level Individual Representation 10/25 Markus Solbach York University Implementation Report: Concurrent Genetic Algorithm with Island Migration

  11. Genetic Algorithm revisited Genetic Algorithm Operators Concurrency Some Results Future Work Mutation Figure : High Level Mutation Representation 11/25 Markus Solbach York University Implementation Report: Concurrent Genetic Algorithm with Island Migration

  12. Genetic Algorithm revisited Genetic Algorithm Operators Concurrency Some Results Future Work Crossover Figure : High Level Crossover Representation 12/25 Markus Solbach York University Implementation Report: Concurrent Genetic Algorithm with Island Migration

  13. Genetic Algorithm revisited Genetic Algorithm Operators Concurrency Some Results Future Work Fitness Function ◮ Travelled distance over all city (inverse fitness) ◮ Each City has a location ( x , y ) ◮ Euclidean distance i =1 ( x i − 1 − x i ) 2 + ( y i − 1 − y i ) 2 ◮ Fitness 1 = � n 13/25 Markus Solbach York University Implementation Report: Concurrent Genetic Algorithm with Island Migration

  14. Genetic Algorithm revisited Genetic Algorithm Operators Concurrency Some Results Future Work Concurrency 14/25 Markus Solbach York University Implementation Report: Concurrent Genetic Algorithm with Island Migration

  15. Genetic Algorithm revisited Genetic Algorithm Operators Concurrency Some Results Future Work Concurrent Random Number Generator Genetic Algorithms rely heavily on random numbers ◮ Math.random() is not concurrent ◮ Multiple threads use similar or same seeds ◮ ThreadLocalRandom 1 ◮ Generator with an internally generated seed ◮ java.util.concurrent.ThreadLocalRandom 1 http://docs.oracle.com/javase/7/docs/api/java/util/ concurrent/ThreadLocalRandom.html 15/25 Markus Solbach York University Implementation Report: Concurrent Genetic Algorithm with Island Migration

  16. Genetic Algorithm revisited Genetic Algorithm Operators Concurrency Some Results Future Work Threads and Islands Each Thread represents one Island ◮ Genetic Algorithm (GA) Logic ◮ Implements Runnable ◮ Concurrent Execution = GA’s Island Migration extension Figure : Simplified UML-Class of CGaimIsland 16/25 Markus Solbach York University Implementation Report: Concurrent Genetic Algorithm with Island Migration

  17. Genetic Algorithm revisited Genetic Algorithm Operators Concurrency Some Results Future Work Threads and Islands Each Thread represents one Island ◮ Genetic Algorithm (GA) Logic ◮ Implements Runnable ◮ Concurrent Execution = GA’s Island Migration extension Figure : Island Migration overview 17/25 Markus Solbach York University Implementation Report: Concurrent Genetic Algorithm with Island Migration

  18. Genetic Algorithm revisited Genetic Algorithm Operators Concurrency Some Results Future Work Threads and Barriers Barriers 2 for synchronization ◮ new CyclicBarrier(# Islands) ◮ Waits that all Islands are ready ◮ Evolution ֒ → random process 2 http://docs.oracle.com/javase/7/docs/api/java/util/ concurrent/CyclicBarrier.html 18/25 Markus Solbach York University Implementation Report: Concurrent Genetic Algorithm with Island Migration

  19. Genetic Algorithm revisited Genetic Algorithm Operators Concurrency Some Results Future Work Island Migration Sequentially (as proposed) . ◮ Dependens on epoch length ◮ Joins all Threads ◮ Avoids shared memory access ◮ Performs cyclic Migration ◮ Copies Individuals ◮ Java’s array.clone()[i] 19/25 Markus Solbach York University Implementation Report: Concurrent Genetic Algorithm with Island Migration

  20. Genetic Algorithm revisited Genetic Algorithm Operators Concurrency Some Results Future Work Island Migration Sequentially (as proposed) . ◮ Dependens on epoch length ◮ Joins all Threads ◮ Avoids shared memory access ◮ Performs cyclic Migration ◮ Copies Individuals ◮ Java’s array.clone()[i] 20/25 Markus Solbach York University Implementation Report: Concurrent Genetic Algorithm with Island Migration

  21. Genetic Algorithm revisited Genetic Algorithm Operators Concurrency Some Results Future Work Some Results 21/25 Markus Solbach York University Implementation Report: Concurrent Genetic Algorithm with Island Migration

  22. Genetic Algorithm revisited Genetic Algorithm Operators Concurrency Some Results Future Work Some Results (Island Migration) Figure : 4 Islands - 150 Individuals - 2 Migrants - 70 Generations Epoch ( ≈ 5 Sec.) 22/25 Markus Solbach York University Implementation Report: Concurrent Genetic Algorithm with Island Migration

  23. Genetic Algorithm revisited Genetic Algorithm Operators Concurrency Some Results Future Work Some Results (Island Migration vs. Sequential) Figure : 100 City - Sequential (150 Individuals) vs. 4 Islands (as before) 23/25 Markus Solbach York University Implementation Report: Concurrent Genetic Algorithm with Island Migration

  24. Genetic Algorithm revisited Genetic Algorithm Operators Concurrency Some Results Future Work Future Work 24/25 Markus Solbach York University Implementation Report: Concurrent Genetic Algorithm with Island Migration

  25. Genetic Algorithm revisited Genetic Algorithm Operators Concurrency Some Results Future Work Future Work ◮ More tests / Parameter improvements ◮ intel i7 (8 cores x 4GHz) using 8 Islands ◮ 5 times faster than sequential GA on same machine ◮ More debugging ◮ bugs in Migration ◮ lack of .clone() ◮ ... ◮ Execution time differences based on number of Islands 25/25 Markus Solbach York University Implementation Report: Concurrent Genetic Algorithm with Island Migration

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