Improvements of Simple Genetic Algorithm in Structural Design

Chen, Ting‐Yu; Chen, Chung-Jei

doi:10.1002/(sici)1097-0207(19970415)40:7<1323::aid-nme117>3.0.co;2-t

Cited by 70 publications

(16 citation statements)

References 11 publications

(8 reference statements)

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“…Since Genetic Algorithm, GA [6] [7], is suitable for a combination optimization problem, GA is used as the optimization algorithm. GA is an optimization algorithm which performs hereditary operations, such as crossover and mutation [8], and can search for a solution suitable for a problem efficiently. GA utilizes some features existed in the data.…”

Section: A Outline Of Proposed Methodsmentioning

confidence: 99%

Classification method into determinable and indeterminable areas using SVM and learning data selection

Hiroyasu

Obori

Yokouchi

2012

The 6th International Conference on Soft Computing and Intelligent Systems, and the 13th International Symposium on Advanced In

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In this paper, a methodology that combines a classification algorithm with a combinatorial optimization problem is proposed. Using the proposed algorithm, classification problems of medical data such as breast cancer data can be performed more accurately. Using the conventional method, the breast cancer data is classified into malignant growth and benign tumor. However, the conventional classification seems insufficient, because there are erroneous cases sometimes, such as the data, having classified into benign, would be found out as malignant, later. To conquer the defect, the proposed method classify the breast cancer data into two groups; (1) the data which definitely belongs to malignant growth area, and (2) the data which has the possibility to belong to either malignant growth area or benign tumor area. Since the libraries of two value classification learning method, used in the conventional method, are highly evaluated in their performance and their easiness of use, the proposed method uses two value classification learning method as well, to achieve the goal. This problem is formulated as combinatorial optimization problem. In this formulation, the malignant growth area is maximized, because of the data which has the possibility to belong to either malignant or benign. The classification algorithm based on learning data selection is proposed, and the effectiveness of the proposed algorithm is discussed through the numerical experiments.

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Section: A Outline Of Proposed Methodsmentioning

confidence: 99%

Classification method into determinable and indeterminable areas using SVM and learning data selection

Hiroyasu

Obori

Yokouchi

2012

The 6th International Conference on Soft Computing and Intelligent Systems, and the 13th International Symposium on Advanced In

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“…Chen and Chen [17] present a study which incorporates elitism into the GA, with several types of crossover (from one to four points) and final local search. It is applied in trusses and also in a topological design with the finite element method (FEM)-77 discretizations-for the optimum design problem of minimum weight.…”

Section: The Late Ninetiesmentioning

confidence: 99%

Optimum Structural Design Using Bio-Inspired Search Methods: A Survey and Applications

Greiner

Emperador²,

Galván³

et al. 2014

Computational Intelligence in Aerospace Sciences

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“…Numerous studies have applied a GA to discrete truss problems. The generation of a good initial population [11], with a combination of a GA with stress heuristics [12] and local search [13] has been proposed to reduce the computational effort. However, in discontinuous cost function optimization such as that mentioned in this study, GA-based methods seem to suffer from redundancy in convergence and dependency on random seeds.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Enhancements of Genetic Algorithm for Minimum Cost Design of Steel Frames Considering Fabrication Cost

Sawada¹,

Matsuo²,

Shimizu³

2012

JCEA

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A discrete optimization problem for minimizing the sum of fabrication cost and steel material cost of steel frames under constraints based on Japanese seismic code is set up. Enhancements of the genetic algorithm for the above-mentioned problem are proposed, which are combined with a 1D (one-dimensional) search or a 2D (two-dimensional) search. After the proposed methods are described, they are applied to a five-story frame. A comparison with an exact solution obtained by a revised enumeration algorithm demonstrates the effectiveness of the proposed methods.

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Improvements of Simple Genetic Algorithm in Structural Design

Cited by 70 publications

References 11 publications

Classification method into determinable and indeterminable areas using SVM and learning data selection

Classification method into determinable and indeterminable areas using SVM and learning data selection

Optimum Structural Design Using Bio-Inspired Search Methods: A Survey and Applications

Enhancements of Genetic Algorithm for Minimum Cost Design of Steel Frames Considering Fabrication Cost

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