Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
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The aim of this paper is to present an original and efficient approach for indentifying material parameter in biomechanics. A new method named GAO (Genetic algorithms & Analytical Optimization) addresses the parameter identification problem that is formulated as a non-linear least-squares problem. To evaluate GAO technique, the identification problem of 7 material parameters of a specific biomechanical law is approached by multiple algorithms (genetic algorithms and gradient-based methods). This comparative demonstrates the rapidity and the efficiency of GAO method in parameter estimation. It also explains the behaviour of genetic algorithms, their efficient operators, and the advantages that GAO method brings to genetics algorithms leading to successful parameter identification.