In this paper a new parallel genetic algorithm for coloring graph vertices is presented. In the algorithm we apply a migration model of parallelism and define two new recombination operators SPPX and CEX. For comparison two problemoriented crossover operators UISX and GPX are selected. The performance of the algorithm is verified by computer experiments on a set of standard graph coloring insta...

This work analyzes the relative advantages of different metaheuristic approaches to the well known natural language processing problem of part-of-speech tagging. This consists of assigning to each word of a text its disambiguated part-of-speech according to the context in which the word is used. We have applied a classic genetic algorithm (GA), a CHC algorithm, and a Simulated Annealing (SA). D...

Many interesting problems in genetic epidemiology are formulated as non-linear optimization problems wing the Gemini/AImini library of routines. Because of the wide availability of networked workstations, we investigate cost-effectively improving the performance of the Gemini/Almini library by exploiting parallelism m’th a set of workstations connected via a local-area network. Instrumentation ...

Implementations of parallel genetic algorithms (GAs) with multiple populations are common, but they introduce several parameters whose effect on the quality of the search is not well understood. Parameters such as the number of populations, their size, the topology of communications, and the migration rate have to be set carefully to reach adequate solutions. This paper presents models that pre...

Genetic algorithms (GAs) are commonly parallelized using multiple communicating populations or by keeping one population and dividing the task of evaluating the tness among several processors. This paper examines an algorithm where the population is physically distributed, but behaves like a single panmictic unit. This is a desirable property because much more is known about single-population G...

This paper analyzes some technical and practical issues concerning the heterogeneous execution of parallel genetic algorithms (PGAs). In order to cope with a plethora of different operating systems, security restrictions, and other problems associated to multi-platform execution, we use Java to implement a distributed PGA model. The distributed PGA runs at the same time on different machines li...

This paper proposes two different parallel genetic algorithms (PGAs) for constrained ordering problems. Constrained ordering problems are constraint optimization problems (COPs) for which it is possible represent a candidate solution as a permutation of objects. A decoder is used to decode this permutation into an instantiafion of the COP vm-iables. Two examples of such constrmnsd ordering prob...

Genetic algorithms (GAs) have several problems, the important of which is that the search ability of ordinary GAs is not always optimal in the early and final stages of the search because of fixed GA parameters. Therefore, we have already proposed the fuzzy adaptive search method for genetic algorithms which is able to tune the genetic parameters according to the search stage by the fuzzy rule....

There is a big need for the parallelisation of genetic algorithms. In this paper, a heterogeneous framework for the global parallelisation of genetic algorithms is presented. The framework uses a static all-worker parallel programming paradigm based on collective communication. It follows the single program multiple data parallel programming model. It utilises the power of parallel machines by ...

The availability of faster and cheaper parallel computers makes it possible to apply genetic algorithms to large populations and very complex applications. This report presents a survey of current implementation techniques for genetic algorithms on parallel hardware.