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Genetic Algorithms on Steroids Using a parallel approach to help evolution along...

Genetic Algorithms on Steroids Using a parallel approach to help evolution along. Asanka Herath & Buddhika Kottahachchi. Motivation. Genetic Algorithm's are generally slow. Constrained by the dependence on randomness to create good genes.

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Genetic Algorithms on Steroids Using a parallel approach to help evolution along...

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  1. Genetic Algorithms on SteroidsUsing a parallel approach to help evolution along... Asanka Herath & Buddhika Kottahachchi

  2. Motivation • Genetic Algorithm's are generally slow. • Constrained by the dependence on randomness to create good genes. • Parallelizing usually just allows the examination of a larger set of candidates – still constrained by randomness to provide good genes.

  3. What if? • Use a different randomized but faster algorithm to generate candidate solutions in parallel. • Inject these candidate solutions into the gene pool of the Genetic Algorithm. • Help the fitness level of the gene pool improve faster than with pure randomization... ... leads to better solutions quicker?

  4. Problem Domain: Bin Packing Rectangles • Given a set of rectangles and the width of a bin, determine the minimum height for a bin containing those rectangles. • Only allow two orientations (ie. Horizontal & Vertical).

  5. Approach • 1 Node devoted as a Coordinator. • Half of the others run a fast simulated annealing algorithm (Candidate Generator). • The rest run independent GA's. • Coordinator polls for candidates and pushes them out to the GA's between GA iterations • Uses C++ and MPI

  6. Validation Method • 60 rectangles (side length up to 100 units)Hopper E. and Turton B. C. H., 2002, "An empirical study of meta-heuristics applied to 2D rectangular bin packing" Special Issue on Cutting, Packing and Knapsacking Problems, Studia Informatica, vol. 2, no. 1 • Bin width 100 units • 4 Nodes/16 virtual Nodes • 50/100 GA iterations, 50 SA iterations per GA iteration.

  7. Results • Control case runs all independent Genetic Algorithms • Test case injects “good genes” into the Genetic Algorithms

  8. Observations • Solutions generated are very close to optimal (~ 10% wastage) • Small improvements required vast amounts of computation • This approach on average yielded about .5% improvement in solution quality (based on wastage).

  9. Conclusions • Given the constraints – the improvement is significant. • Problem selected to test hypothesis – non-ideal? • This approach merits further investigation • Other problem domains

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