Optimum design of trusses using a genetic algorithm
Computational engineering using metaphors from nature
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Biases in the Crossover Landscape
Proceedings of the 3rd International Conference on Genetic Algorithms
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Finite Elements in Analysis and Design
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Weight minimization of trusses with genetic algorithm
Applied Soft Computing
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In this study, the use of new alternatives of encoding types such as quaternary encoding and octal encoding is reviewed along with that of binary and value encodings and how they provide contribution to efficiency and robustness of genetic algorithm is discussed in structural problems. In addition to new alternatives of encoding types, a genetic algorithm with adaptive manner is presented so that an adaptive approach including adaptive mutation and adaptive crossover operators is employed. It is concluded that it is not possible to tell that one of the encoding types is exactly dominant over the others in all aspects such as convergence, finding the optimum solution, and iteration number. However, it is worth to say that to run genetic process with different encoding types should be considered since one of the runs can give more appropriate solution than that of the others.