A Parallel Implementation of a Multi-objective Evolutionary Algorithm

1. A PARALLEL IMPLEMENTATION OF A
MULTI-OBJECTIVE EVOLUTIONARY
ALGORITHM
ITAB 2009
Christos C. Kannas, Christos A. Nicolaou,
Constantinos S. Pattichis
University of Cyprus and Noesis Cheminformatics

2. OUTLINE
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 Introduction
 Background
 Graph Based Evolutionary Algorithms
 Parallel Evolutionary Algorithms
 Methodology
 Multi-objective Evolutionary Graph Algorithm
 Parallel Multi-objective Evolutionary Graph Algorithm
 Results
 Conclusion
 Questions ???
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3. INTRODUCTION
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 Multi-objective Evolutionary Algorithms (MOEAs).
 Single-objective Problems  Single optimal
solution.
 Multi-objective Problems  Set of equivalent
solutions, Pareto-front.
 Parallel Evolutionary Algorithms  Parallel
Processing:
 General Purpose Graphical Processing Units
(GPGPUs)
 Multi- and Many-Core CPUs.
 Clusters.
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4. BACKGROUND
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 Graph Based Evolutionary Algorithms:
 Graph G(V, E).
 Mutations:
 Flip Vertex/Edge.
 Remove Vertex/Edge.
 Add Vertex/Edge.
 Problem specific mutations.
 Add/Remove Ring.
 Add/Remove/Exchange Fragment.
 Crossover:
 Recombination of Subgraphs.
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5. BACKGROUND
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 Parallel Evolutionary Algorithms (PEAs)
 Coarse-grained PEAs:
 Several Subpopulations.
 Isolation time.
 Migration:
 Uniformly at random.
 Fitness based.
 Migration Scheme:
 Complete unrestricted net topology.
 Ring topology.
 Neighbourhood topology.
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6. BACKGROUND
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 Parallel Evolutionary Algorithms (PEAs)
 Coarse-grained PEA with Complete net topology.
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7. BACKGROUND
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 Parallel Evolutionary Algorithms (PEAs)
 Coarse-grained PEA with Ring topology.
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8. BACKGROUND
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 Parallel Evolutionary Algorithms (PEAs)
 Coarse-grained PEA with Neighbourhood topology.
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9. BACKGROUND
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 Parallel Evolutionary Algorithms (cont.)
 Fine-grained PEAs:
 Master – Slave.
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10. METHODOLOGY
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 Multi-objective Evolutionary Graph Algorithm
(MEGA)
 Chromosomes  Graphs.
 MEGA Workflow:
 Working Population.
 Fitness Calculation.
 Hard Filter.
 Pareto Ranking.
 Efficiency Calculation.
 Parents Selection.
 Evolve Parents.
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11. METHODOLOGY
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 Multi-objective Evolutionary Graph Algorithm (cont.)
6. Evolve Working
Parents Population
5. Select 1. Fitness
Parents Calculation
4.
2. Hard
Efficiency
Filter
Calculation
3. Pareto 11
Ranking

12. METHODOLOGY
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 Parallel Multi-objective Evolutionary Algorithm
(PMEGA)
 Python:
 Threads  Global Interpreter Lock (GIL).
 Processes  Spawning multiple processes (Our approach).
 3rd Party add-ons, MPI4PY, PyCUDA, PyOpenCL.
 Key facts:
 A set of subpopulations. 2 subpopulations, although this is a
parameter that can change.
 A pool of processes. 2 cores  2 processes for simultaneous
execution.
 Execution path is the same as MEGA.
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13. METHODOLOGY
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 Parallel Multi-objective Evolutionary Algorithm
(PMEGA) (cont.)
 PMEGA Workflow.
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14. RESULTS
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 Testing PC:
 Intel Core 2 Duo E8400 @ 3.0 GHz
 4 Gbytes RAM
 Experiment Setup:
 Population 100. (BioAssay 713)
 Iterations 200.
 5 Runs per experiment.
 2 Objectives:
 Similarity on 3 ligands, selective to ER-b.
 Dissimilarity on 2 ligands, selective to ER-a.
 3662 Building blocks, fragments taken from compounds
of BioAssay 1211. 14

15. RESULTS (CONT.)
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 Time Results
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16. RESULTS (CONT.)
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 Pareto Front from MEGA
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17. RESULTS (CONT.)
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 Pareto Front from PMEGA
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18. RESULTS (CONT.)
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 MEGA vs. PMEGA
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19. CONCLUSION
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 Quality of solutions:
 MEGA and PMEGA behave comparably. Using a better
way to split the subpopulations might result in better
results for PMEGA.
 Execution time:
 PMEGA 1.6 times faster than MEGA on a 2 core CPU.
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20. QUESTIONS ???
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