Evopar12 Validating a P2P EA
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This document describes research validating a peer-to-peer evolutionary algorithm model in a real-world environment. The researchers conducted large-scale experiments on over 3008 computing agents distributed across 188 computers to test the scalability of their peer-to-peer evolutionary computation model. Their results showed seamless scalability up to the large number of agents used. Compared to prior simulation-based studies, the experiments validated that the parallel evolutionary algorithm could solve problems with similar or better performance than sequential algorithms, while achieving near-linear speedup through massive parallelization.
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Dr. Ji Liu received his Ph.D in computer science and his bachelor degree in automation from University of Wisconsin-Madison and University of Science and Technology of China, respectively. After graduation, he joined the University of Rochester as an assistant professor, conducting research in machine learning, optimization, and reinforcement learning. The developed asynchronous and decentralized algorithms were widely used in industry, such as IBM, Microsoft, etc. He left academia and joined Tencent in 2017, exploring AI’s boundary. The developing AI agent Tstarbot was considered to be a milestone for mastering the most challenging RTS game -- Starcraft II. His second stop in industry is Kwai - the second largest short video company in China. He founded and led multiple international teams with different functionalities: platform team, product team, and research team. His team Contributed to 15+% annual revenue growth in Ads. He published 100+ papers in top-tier CS conferences and journals, and received multiple best paper awards (e.g., SIGKDD 2010 and UAI 2015 Facebook best paper). He was an awardee of MIT TR 35 under 35 in China and IBM faculty award in 2017. He was nominated to be one of China top 5 AI innovators under 35 in 2018
Rajesh - CV
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Evopar12 Validating a P2P EA
- 1. Validating a Peer-to-Peer Evolutionary Algorithm J.L.J. Laredo*, P. Bouvry, S. Mostaghim, J.J. Merelo EvoPAR 2012, Wednesday 11th April, 2012 Malaga, Spain
- 2. P2P Optimization What? Why? Parallelization of time-consuming meta-heuristics in P2P systems: Branch&Bound Massive Scalability Shorten convergence PSO EA … time BUT!! So far… simulation based experimentation
- 3. Outline Introduction The Curse of Dimensionality Parallel vs. Sequential P2P EC Model Viability Validation in a real-environment Simulations Algorithmic results Massive scalability Conclusions
- 4. The Curse of Dimensionality Introduction - P2P EC Model - Validation - Conclusions
- 5. The Curse of Dimensionality Introduction - P2P EC Model - Validation - Conclusions
- 6. The Curse of Dimensionality Introduction - P2P EC Model - Validation - Conclusions
- 7. Parallel vs. Sequential Introduction - P2P EC Model - Validation - Conclusions
- 8. Parallel vs. Sequential Introduction - P2P EC Model - Validation - Conclusions
- 9. Evolvable Agent Overlay Network Agent-based approach Fine-grain parallelization Spatially structured EA Local Selection Physical Network Introduction - P2P EC Model - Validation - Conclusions
- 10. Viability Simulator J.L.J. Laredo, A.E. Eiben, M. van Steen, J. J. Merelo. EvAg: a scalable peer-to-peer evolutionary algorithm. Genetic Programming and Evolvable Machines , 11(2):227-246. 2010. Simulator conducted experimentation!!! Introduction - P2P EC Model - Validation - Conclusions
- 11. Simulations Simulator Instance P Avg. N. Max. N. Gen Gen. L=48 390 85 140 Simulator conducted L=150 3000 173 250 experimentation!!! Introduction - P2P EC Model - Validation - Conclusions
- 12. Algorithmic Results https://forja.rediris.es/svn/geneura/drmWrapper Instance P Avg. N. Max. N. Gen Gen. http://www.hlrs.de/systems/platforms/nec-nehalem-cluster L=48 390 85 140 Introduction - P2P EC Model - Validation - Conclusions
- 13. Massive Scalability https://forja.rediris.es/svn/geneura/drmWrapper http://www.hlrs.de/systems/platforms/nec-nehalem-cluster Introduction - P2P EC Model - Validation - Conclusions
- 14. Conclusions 1st large parallel P2P EA experiment 188 computers x 8 cores x 2 threads = 3008 agents Seamless scalability Conducted experimentation meets simulations Generations Pop. Size S.R. Simulator 85 390 0.98 Parallel 40-80 400 0.8 T seq Massive scalability: Speed up T par 1000 Introduction - P2P EC Model - Validation - Conclusions
- 15. Thanks for your attention!!