Abstract
The growing success of biologically inspired algorithms in solving large and complex problems has spawned many interesting areas of research. Over the years, one of the mainstays in bio-inspired research has been the exploitation of parallel and distributed environments to speedup computations and to enrich the algorithms. From the early days of research on bio-inspired algorithms, their inherently parallel nature was recognized and different parallelization approaches have been explored. Parallel algorithms promise reductions in execution time and open the door to solve increasingly larger problems. But parallel platforms also inspire new bio-inspired parallel algorithms that, while similar to their sequential counterparts, explore search spaces differently and offer improvements in solution quality.
Keywords
- Graphic Processing Unit
- Single Instruction Multiple Data
- Distribution Algorithm
- Volunteer Computing
- Single Program Multiple Data
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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de Vega, F.F., Cantú-Paz, E. (2010). Introduction. In: de Vega, F.F., Cantú-Paz, E. (eds) Parallel and Distributed Computational Intelligence. Studies in Computational Intelligence, vol 269. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10675-0_1
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