A message passing standard for MPP and workstations
Communications of the ACM
Models and languages for parallel computation
ACM Computing Surveys (CSUR)
Communications of the ACM
Parallel programming: techniques and applications using networked workstations and parallel computers
A greedy genetic algorithm for the quadratic assignment problem
Computers and Operations Research
Parallel programming in OpenMP
Parallel programming in OpenMP
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Parallel Computer Architecture: A Hardware/Software Approach
Parallel Computer Architecture: A Hardware/Software Approach
Migration Policies, Selection Pressure, and Parallel Evolutionary Algorithms
Journal of Heuristics
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Paper: Robust taboo search for the quadratic assignment problem
Parallel Computing
Proceedings of the 2010 ACM Symposium on Applied Computing
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Many problems in the operations research field cannot be solved to optimality within reasonable amounts of time with current computational resources. In order to find acceptable solutions to these computationally demanding problems, heuristic methods such as genetic algorithms are often developed. Parallel computing provides alternative design options for heuristic algorithms, as well as the opportunity to obtain performance benefits in both computational time and solution quality of these heuristics. Heuristic algorithms may be designed to benefit from parallelism by taking advantage of the parallel architecture. This study will investigate the performance of the same global parallel genetic algorithm on two popular parallel architectures to investigate the interaction of parallel platform choice and genetic algorithm design. The computational results of the study illustrate the impact of platform choice on parallel heuristic methods. This paper develops computational experiments to compare algorithm development on a shared memory architecture and a distributed memory architecture. The results suggest that the performance of a parallel heuristic can be increased by considering the desired outcome and tailoring the development of the parallel heuristic to a specific platform based on the hardware and software characteristics of that platform.