Massively Parallel Genetic Algorithms
Desra Ghazfan, Balakrishnan Srinivasan, Mark Nolan, Caulfield Vic · 1994
Heuristic algorithms are usually employed to find an optimal solution to NP-Complete problems. Genetic algorithms are among such algorithms and they are search algorithms based on the mechanics of natural selection and genetics. Since genetic algorithms work with a set of candidate solutions, parallelisation based on the SIMD paradigm seems to be the natural way to obtain a speed up. In this approach, the population of strings is distributed among the processing elements. Each of the strings is then processed independently of the other. The performance gain for this approach comes from the parallel execution of the strings, and hence, it is heavily dependent on the population size. The approach is favoured for genetic algorithms' applications where the parameter set for a particular run is well-known in advance, and where such applications require a big population size to solve the problem. DDAP fits nicely into the above requirements. The aim of the parallelisation is two-fold: the first one is to speedup the allocation process in DDAP which usually consists of thousands of documents and has to use a big population size, and second, it can be seen as an attempt to port the genetic algorithm's processes into SIMD machines.