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This paper describes a fine-grained parallel genetic algorithm, MPGAIA (Massively Parallel Genetic Algorithm for Image Analysis), that was implemented on a SIMD type parallel computer, a DAP 510. We then describe using MPGAIA to evolve filters, which when integrated with a simple classifier program are applied to a subset of a real-world problem wh...
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... each P.C. or workstation must use the same protocol, or message passing library, such as PVM (Sunderam 1989) or P4 (Butler andLusk 1994). Figure 2 shows a typical MIMD architecture. CGPGAs are designed to utilise the power of MIMD-style machines by minimising the synchronisation of the processors and the communication required between the processors. ...
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Existing techniques for mapping image data onto the processors of a SIMD machine are suitable for algorithms which operate on data of spatially regular patterns. However, many algorithms for image analysis deal with data on irregular and/or dynamically varying regions. We present a new data mapping technique called reflective-folding which can cate...
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One of the main reasons for using parallel evolutionary algorithms (PEAs) is to obtain efficient algorithms with an execution time much lower than that of their sequential counterparts in order, e.g., to tackle more complex problems. This naturally leads to measuring the speedup of the PEA. PEAs have sometimes been reported to provide super-linear performances for different problems, parameterizations, and machines. Super-linear speedup means that using “m” processors leads to an algorithm that runs more than “m” times faster than the sequential version. However, reporting super-linear speedup is controversial, especially for the “traditional” research community, since some non-orthodox practices could be thought of being the cause for this result. Therefore, we begin by offering a taxonomy for speedup, in order to clarify what is being measured. Also, we analyze the sources for such a scenario in this paper. Finally, we study an assorted set of results. Our conclusion is that super-linear performance is possible for PEAs, theoretically and in practice, both in homogeneous and in heterogeneous parallel machines.
In this paper, we present quantitative models for the selection pressure of cellular evolutionary algorithms on regular one- and two-dimensional (2-D) lattices. We derive models based on probabilistic difference equations for synchronous and several asynchronous cell update policies. The models are validated using two customary selection methods: binary tournament and linear ranking. Theoretical results are in agreement with experimental values, showing that the selection intensity can be controlled by using different update methods. It is also seen that the usual logistic approximation breaks down for low-dimensional lattices and should be replaced by a polynomial approximation. The dependence of the models on the neighborhood radius is studied for both topologies. We also derive results for 2-D lattices with variable grid axes ratio.
This paper studies static and dynamic decentralized versions of the search model known as cellular genetic algorithm (cGA), in which individuals are located in a specific topology and interact only with their neighbors. Making changes in the shape of such topology or in the neighborhood may give birth to a high number of algorithmic variants. We perform these changes in a methodological way by tuning the concept of ratio. Since the relationship (ratio) between the topology and the neighborhood shape defines the search selection pressure, we propose to analyze in depth the influence of this ratio on the exploration/exploitation tradeoff. As we will see, it is difficult to decide which ratio is best suited for a given problem. Therefore, we introduce a preprogrammed change of this ratio during the evolution as a possible additional improvement that removes the need of specifying a single ratio. A later refinement will lead us to the first adaptive dynamic kind of cellular models to our knowledge. We conclude that these dynamic cGAs have the most desirable behavior among all the evaluated ones in terms of efficiency and accuracy; we validate our results on a set of seven different problems of considerable complexity in order to better sustain our conclusions.
