Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
The Evolution of Agents that Build Mental Models and Create Simple Plans Using Genetic Programming
Proceedings of the 6th International Conference on Genetic Algorithms
A Segregated Genetic Algorithm for Constrained Structural Optimization
Proceedings of the 6th International Conference on Genetic Algorithms
Distributed genetic algorithms for the floorplan design problem
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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This paper presents a structural application of a shape optimization method based on a Genetic Algorithm (GA). The method produces a sequence of fixed-distance step-wise movements of the boundary nodes of a finite element model to derive optimal shapes from an arbitrary initial design space. The GA is used to find the optimal or near-optimal combination of boundary nodes to be moved for a given step movement. The GA uses both basic and advanced operators. For illustrative purposes, the method has been applied to structural shape-optimization. The shape-optimization methodology presented allows local optimization, where only crucial parts of a structure are optimized as well as global shape-optimization which involves finding the optimal shape of the structure as a whole for a given environment as described by its loading and freedom conditions. Material can be removed or added to reach the optimal shape. Two examples of structural shape optimization are included showing local and global optimization through material removal and addition.