Multi-objective Optimization using
Genetic Algorithms: A Tutorial.
Three localization algorithms for solving the optimization problem are compared: simplex, AMA and
genetic algorithms. This study shows that the choice of an algorithm depends on the desired accuracy and computation time.
This is because historically speaking, the process of natural selection is perceived to be a 'slow' procedure, although this view should have been refined when John Holland, one of the principal researchers and founder of
genetic algorithms, published the schema theorem in 1975.
The
genetic algorithms (GA) are categorized as evolutionary algorithms that are types of Artificial Intelligence (AI) technique.
Genetic Algorithms (GA) mimic the processes observed in natural evolution, like natural selection and genetics, following the principles of first laid down by Charles Darwin of "survival of the fittest".
In recent years, the geotechnical engineers are also using
genetic algorithms (GAs) for the computerisation of their many optimisation problems.
Studies aiming at simulation and optimization with the oat crop through AI via artificial neural networks and
genetic algorithms are inexistent in the Brazilian research, although they can contribute to important processes related to the management of the species.
Lee, "Adaptive Crossover, Mutation and Selection Using Fuzzy system for
Genetic Algorithms," Artificial Life and Robotics, vol.
We use
genetic algorithms for multiple computations.