Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A comparison of the fixed and floating building block representation in the genetic algorithm
Evolutionary Computation
Implicit representation in genetic algorithms using redundancy
Evolutionary Computation
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Performing synthesis during conceptual design provides substantial cost savings by selecting an efficient design topology and geometry, in addition to selecting the structural member properties. A new evolutionary-based representation, which combines redundancy and implicit fitness constraints, is introduced to represent and search for design solutions in an unstructured, multi-objective structural frame problem. The implicit redundant representation genetic algorithm, in tandem with the unstructured problem domain definition, allows the evaluation of diverse frame topologies and geometries. The IRR GA allows the representation of a variable number of location independent parameters, which overcomes the fixed parameter limitations of standard GAs. The novel frame designs evolved by the IRR GA synthesis design method compare favourably with traditional frame design solutions calculated by trial and error.