Massive parallelization of the compact genetic algorithm (original) (raw)

An Architecture for Massive Parallelization of the Compact Genetic Algorithm

Lecture Notes in Computer Science, 2004

This paper presents an architecture which is suitable for a massive parallelization of the compact genetic algorithm. The resulting scheme has three major advantages. First, it has low synchronization costs. Second, it is fault tolerant, and third, it is scalable. The paper argues that the benefits that can be obtained with the proposed approach is potentially higher than those obtained with traditional parallel genetic algorithms. In addition, the ideas suggested in the paper may also be relevant towards parallelizing more complex probabilistic model building genetic algorithms.

Efficient Parallel Genetic Algorithms: Theory and Practice

2000

Parallel genetic algorithms (GAs) are complex programs that are controlled by many parameters, which a ect their search quality and their e ciency. The goal of this paper is to provide guidelines to choose those parameters rationally. The investigation centers on the sizing of populations, because previous studies show that there is a crucial relation between solution quality and population size. As a rst step, the paper shows how to size a simple GA to reach a solution of a desired quality. The simple GA is then parallelized, and its execution time is optimized. The rest of the paper deals with parallel GAs with multiple populations. Two bounding cases of the migration rate and topology are analyzed, and the case that yields good speedups is optimized. Later, the models are specialized to consider sparse topologies and migration rates that are more likely to be used by practitioners. The paper also presents the additional advantages of combining multi-and single-population parallel GAs. The results of this work are simple models that practitioners may use to design e cient and competent parallel GAs.

The Parallel Genetic Algorithm Embedded with Downhill

Algorithms and Architectures for Parallel Processing - Proceedings of the 4th International Conference, 2000

Alogrithm (PGA) embedded with Downhill which is applied to the optimization of continuous functions. The strategy goes this way: Subpopulation tries to locate good local minima by PGA. When a subpopulation does not progress after a certain number of generations, downhill is taken into account. Alternative downhill method (depth first or width first) is to be used depending on the properties of different problems. At certain generation, local optima of each nodes are transmitted to each other with the support of MPI environment. Selection of genetic factors is discussed and comparison with traditional GA is made to illustrate the effectiveness of the algorithm. On the whole, the hybrid algorithm strives to gain breakthrough in the field of large scale computation and as a matter of fact it turns out to be quite successful.

Scalable Parallel Genetic Algorithms

Artificial Intelligence Review, 2001

Genetic algorithms, search algorithms based on the genetic processes observed in natural evolution, have been used to solve difficult problems in many different disciplines. When applied to very large-scale problems, genetic algorithms exhibit high computational cost and degradation of the quality of the solutions because of the increased complexity. One of the most relevant research trends in genetic algorithms is

A Heterogeneous Framework for the Global Parallelisation of Genetic Algorithms

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 allowing multiple crossover and mutation operators being used within a single genetic algorithm. This mixture of operators can be applied to the strings of a population in parallel without changes to the canonical sequential genetic algorithm. These features help the parallel genetic algorithm in exploiting the search space efficiently and thoroughly when compared to the sequential genetic algorithm. The framework is instantiated with specific parameters to solve an NP-hard problem, the asymmetric travelling salesman problem. The results for the parallel genetic algorithm are very good in term...

Analysis of the Numerical Effects of Parallelism on a Parallel Genetic Algorithm

1996

Examines the effects of relaxed synchronization on both the numerical and parallel efficiency of parallel genetic algorithms (GAs). We describe a coarse-grain geographically structured parallel genetic algorithm. Our experiments provide preliminary evidence that asynchronous versions of these algorithms have a lower run-time than synchronous GAs. Our analysis shows that this improvement is due to (1) reduced synchronization costs and (2) higher numerical efficiency (e.g. fewer function evaluations) for the asynchronous GAs. This analysis includes a critique of the utility of traditional parallel performance measures for parallel GAs

An Asynchronous Model of Global Parallel Genetic Algorithms

2000

Genetic algorithms usually require more computation power than other heuristic approaches do. In this paper we introduce an efficient implementation of asynchronously global parallel genetic algorithm with 3-tournament elimination selection. The parallelization of the algorithm is achieved through multithreading mechanism, a very effective and easy to implement technique. With parallelization we can get a significant decrease in computational time on a multiprocessor system. Reducing interprocess communication is a key to getting high performance in parallel computing. That is the reason why the asynchronous model is used.

A Survey of Parallel Genetic Algorithms

1997

Genetic algorithms (GAs) are powerful search techniques that are used successfully to solve problems in many different disciplines. Parallel GAs are particularly easy to implement and promise substantial gains in performance. As such, there has been extensive research in this field. This survey attempts to collect, organize, and present in a unified way some of the most representative publications on parallel genetic algorithms. To organize the literature, the paper presents a categorization of the techniques used to parallelize GAs, and shows examples of all of them. However, since the majority of the research in this field has concentrated on parallel GAs with multiple populations, the survey focuses on this type of algorithms. Also, the paper describes some of the most significant problems in modeling and designing multi-population parallel GAs and presents some recent advancements.