Development of a grid-aware master worker framework for artificial evolution

Ketenci, Ahmet
Genetic Algorithm (GA) has become a very popular tool for various kinds of problems, including optimization problems with wider search spaces. Grid search techniques are usually not feasible or ineffective at finding a solution, which is good enough. The most computationally intensive component of GA is the calculation of the goodness (fitness) of candidate solutions. However, since the fitness calculation of each individual does not depend each other, this process can be parallelized easily. The easiest way to reach high amounts of computational power is using grid. Grids are composed of multiple clusters, thus they can offer much more resources than a single cluster. On the other hand, grid may not be the easiest environment to develop parallel programs, because of the lack of tools or libraries that can be used for communication among the processes. In this work, we introduce a new framework, GridAE, for GA applications. GridAE uses the master worker model for parallelization and offers a GA library to users. It also abstracts the message passing process from users. Moreover, it has both command line interface and web interface for job management. These properties makes the framework more usable for developers even with limited parallel programming or grid computing experience. The performance of GridAE is tested with a shape optimization problem and results show that the framework is more convenient to problems with crowded populations.


Multiobjective evolutionary feature subset selection algorithm for binary classification
Deniz Kızılöz, Firdevsi Ayça; Coşar, Ahmet; Dökeroğlu, Tansel; Department of Computer Engineering (2016)
This thesis investigates the performance of multiobjective feature subset selection (FSS) algorithms combined with the state-of-the-art machine learning techniques for binary classification problem. Recent studies try to improve the accuracy of classification by including all of the features in the dataset, neglecting to determine the best performing subset of features. However, for some problems, the number of features may reach thousands, which will cause too much computation power to be consumed during t...
A Customized force-directed layout algorithm for biological graphs whose vertices have enzyme commission attributes
Danacı, Hasan Fehmi; Atalay, Mehmet Volkan; Department of Computer Engineering (2015)
Force directed layout algorithm is popularly used to draw biological graphs. However, it employs only graph structure. When we would like to embed domain-specific knowledge, such as biological or chemical attributes related to the vertices, force directed layout algorithm should be modified. It is then important to draw more readable layouts for biologists without the dispose of aesthetically pleasing way that comes from force-directed algorithm’s nature. This thesis aims to describe a modified and improved...
A method for chromosome handling of r-permutations of n-element set in genetic algorithms
Üçoluk, Göktürk (1997-04-16)
Combinatorial optimisation problems are in the domain of Genetic Algorithms (GA) interest. Unfortunately ordinary crossover and mutation operators cause problems for chromosome representations of permutations and some types of combinations. This is so because offsprings generated by means of the ordinary operators are of a great possibility no more valid chromosomes. A variety of methods and new operators that handle that sort of obscenities are introduced throughout the literature. A new method for represe...
Evaluation of crossover techniques in genetic algorithm based optimum structural design
Hasançebi, Oğuzhan (2000-11-01)
Crossover is one of the three basic operators in any genetic algorithm (GA). Several crossover techniques have been proposed and their relative merits are currently under investigation. This paper starts with a brief discussion of the working scheme of the GAs and the crossover techniques commonly used in previous GA applications. Next, these techniques are tested on two truss size optimization problems, and are evaluated with respect to exploration and exploitation aspects of the search process. Finally, t...
Using social graphs in one-class collaborative filtering problem
Kaya, Hamza; Alpaslan, Ferda Nur; Department of Computer Engineering (2009)
One-class collaborative filtering is a special type of collaborative filtering methods that aims to deal with datasets that lack counter-examples. In this work, we introduced social networks as a new data source to the one-class collaborative filtering (OCCF) methods and sought ways to benefit from them when dealing with OCCF problems. We divided our research into two parts. In the first part, we proposed different weighting schemes based on social graphs for some well known OCCF algorithms. One of the weig...
Citation Formats
A. Ketenci, “Development of a grid-aware master worker framework for artificial evolution,” M.S. - Master of Science, Middle East Technical University, 2010.