Evaluation of crossover techniques in genetic algorithm based optimum structural design

Date

2000-11-01

Author

Hasançebi, Oğuzhan

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

193
views

0
downloads

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, the paper proposes two newly developed crossover techniques, through which a better efficiency of GAs can be obtained. Comparative studies are carried out between the proposed and the common crossover techniques, and the results are fully discussed. (C) 2000 Civil-Comp Ltd. and Elsevier Science Ltd. All rights reserved.

Subject Keywords

Optimization, Structural Optimization, Genetic Algorithms, Crossover Techniques

URI

https://hdl.handle.net/11511/36483

Journal

COMPUTERS & STRUCTURES

DOI

https://doi.org/10.1016/s0045-7949(00)00089-4

Collections

Department of Civil Engineering, Article

Suggestions

OpenMETU
Core

Evaluation of crossover techniques in genetic algorithm based optimum structural design Hasançebi, Oğuzhan (1998-08-21) Crossover is one of the three basic operators in ally 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 genetic algorithms (GAs) and 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 proce...
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...
Development of a grid-aware master worker framework for artificial evolution Ketenci, Ahmet; Şener, Cevat; Department of Computer Engineering (2010) 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 wa...
Efficient and Accurate Electromagnetic Optimizations Based on Approximate Forms of the Multilevel Fast Multipole Algorithm Onol, Can; Karaosmanoglu, Bariscan; Ergül, Özgür Salih (2016-01-01) We present electromagnetic optimizations by heuristic algorithms supported by approximate forms of the multilevel fast multipole algorithm (MLFMA). Optimizations of complex structures, such as antennas, are performed by considering each trial as an electromagnetic problem that can be analyzed via MLFMA and its approximate forms. A dynamic accuracy control is utilized in order to increase the efficiency of optimizations. Specifically, in the proposed scheme, the accuracy is used as a parameter of the optimiz...
Impact of crossover and decision variable coding forms on the performance of genetic algorithm optimization a case study on pump and treat remediation design GÜNGÖR DEMİRCİ, Gamze; Aksoy, Ayşegül (2008-05-23) Genetic algorithms (GAs) are robust methods applied especially for complex optimization problems. However, selection of some GA parameters may play a vital role on the efficacy of the method. This study investigates the impacts of crossover type and form of coding on the performance of optimization by genetic algorithms. For this purpose, two pump-and-treat remediation design problems were considered with different levels of complexity determined by the number of decision variables. The efficiencies of GA f...

Citation Formats

O. Hasançebi, “Evaluation of crossover techniques in genetic algorithm based optimum structural design,” COMPUTERS & STRUCTURES, pp. 435–448, 2000, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/36483.