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.


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Lokman, Banu (2019-01-01)
We develop an evolutionary algorithm for multiobjective combinatorial optimization problems. The algorithm aims at converging the preferred solutions of a decision-maker. We test the performance of the algorithm on the multiobjective knapsack and multiobjective spanning tree problems. We generate the true nondominated solutions using an exact algorithm and compare the results with those of the evolutionary algorithm. We observe that the evolutionary algorithm works well in approximating the solutions in the...
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Karagoz, Osman Kaan; Ankaralı, Mustafa Mert (2018-01-01)
In this paper, we present the comparison of different optimization algorithms that are used to optimize the parameters of a simulated legged robotic platform. We compare the results obtained by applying different algorithms on the same model and show the relative advantages and disadvantages of these algorithms. The tested algorithms are Particle Swarm Optimization, Binary Coded Genetic Algorithm, Broyden-Fletcher-Goldfrab-Shannon Algorithm and Method of Zoutendijk. We showed that the globally optimal param...
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.