Massively Parallel Evolutionary Computation on GPGPUs

  • Shigeyoshi Tsutsui
  • Pierre Collet

Part of the Natural Computing Series book series (NCS)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Tutorials

    1. Front Matter
      Pages 1-1
    2. Pierre Collet
      Pages 3-14
    3. Ogier Maitre
      Pages 15-34
  3. Implementations of Various EAs

    1. Front Matter
      Pages 61-61
    2. Frédéric Krüger, Ogier Maitre, Santiago Jiménez, Laurent A. Baumes, Pierre Collet
      Pages 63-81
    3. Asim Munawar, Mohamed Wahib, Masaharu Munetomo, Kiyoshi Akama
      Pages 83-104
    4. Shigeyoshi Tsutsui, Noriyuki Fujimoto
      Pages 105-120
    5. Pavel Krömer, Jan Platoš, Václav Snášel, Ajith Abraham
      Pages 121-147
    6. Steven Solomon, Parimala Thulasiraman, Ruppa K. Thulasiram
      Pages 149-178
    7. Shigeyoshi Tsutsui, Noriyuki Fujimoto
      Pages 179-202
    8. Martín Pedemonte, Francisco Luna, Enrique Alba
      Pages 203-225
    9. Simon Harding, Julian F. Miller
      Pages 249-266
  4. Applications

    1. Front Matter
      Pages 309-309
    2. Martin Simonsen, Mikael H. Christensen, René Thomsen, Christian N. S. Pedersen
      Pages 349-367
    3. Laurent A. Baumes, Frédéric Krüger, Pierre Collet
      Pages 369-387
    4. Lidia Yamamoto, Pierre Collet, Wolfgang Banzhaf
      Pages 389-419
    5. Yuji Sato, Naohiro Hasegawa, Mikiko Sato
      Pages 421-444
  5. Back Matter
    Pages 445-453

About this book


Evolutionary algorithms (EAs) are metaheuristics that learn from natural collective behavior and are applied to solve optimization problems in domains such as scheduling, engineering, bioinformatics, and finance. Such applications demand acceptable solutions with high-speed execution using finite computational resources. Therefore, there have been many attempts to develop platforms for running parallel EAs using multicore machines, massively parallel cluster machines, or grid computing environments. Recent advances in general-purpose computing on graphics processing units (GPGPU) have opened up this possibility for parallel EAs, and this is the first book dedicated to this exciting development.


The three chapters of Part I are tutorials, representing a comprehensive introduction to the approach, explaining the characteristics of the hardware used, and presenting a representative project to develop a platform for automatic parallelization of evolutionary computing (EC) on GPGPUs. The ten chapters in Part II focus on how to consider key EC approaches in the light of this advanced computational technique, in particular addressing generic local search, tabu search, genetic algorithms, differential evolution, swarm optimization, ant colony optimization, systolic genetic search, genetic programming, and multiobjective optimization. The six chapters in Part III present successful results from real-world problems in data mining, bioinformatics, drug discovery, crystallography, artificial chemistries, and sudoku.


Although the parallelism of EAs is suited to the single-instruction multiple-data (SIMD)-based GPU, there are many issues to be resolved in design and implementation, and a key feature of the contributions is the practical engineering advice offered. This book will be of value to researchers, practitioners, and graduate students in the areas of evolutionary computation and scientific computing.


Artificial chemistries CGP Cartesian genetic programming Clusters Differential evolution Evolutionary computation GA GP GPGPU GPU General-purpose computing on graphics processing units Genetic algorithms Genetic programming Graphics processing unit Many-core processors Massively parallel multiobjective optimization Memetic algorithm Microprocessors PSO Parallel metaheuristics Parallelization Particle swarm optimization SIMD Scientific computing Single-instruction multiple-data processors

Editors and affiliations

  • Shigeyoshi Tsutsui
    • 1
  • Pierre Collet
    • 2
  1. 1.and Information ScienceHannan University Dept. of ManagementMatsubara, OsakaJapan
  2. 2.ICube Laboratory UMR CNRS 7357Université de StrasbourgIllkirchFrance

Bibliographic information