Genetic Algorithms

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Genetic Algorithms

Models that optimize rules by mimicking the Darwinian Law of survival of the fittest. A set of rules is chosen from those that work the best. The weakest are discarded. In addition, two successful rules can be combined (the equivalent to genetic cross-overs) to produce offspring rules. The offspring can replace the parents, or they will be discarded if less successful than the parents. Mutation is also accomplished by randomly changing elements. Mutation and cross-over occur with low probability, as in nature.
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Keywords: Genetic algorithm, Adsorption isotherm, Adsorption of phenol, Adsorption kinetics.
How to find out relationship between individual coding length and classification accuracy and the convergence speed in theory, and thus more effectively adjust the individual coding length of the classifier of the genetic algorithm, has become an urgent problem to be solved which can be modelling as formula 10.
According to CCR, the results have shown that genetic algorithm is more accurate than Masek algorithm.
In another hand, the genetic algorithm gives better results with 32% of event located with an error inferior to 2 cm against 27% for simplex and 26.3% for AMA.
Evaluation of crossover techniques in genetic algorithm based optimum structural design.
As a matter of fact, a conventional genetic algorithm for the implementation of the proposed algorithm was used, but it was adapted with two pointers whose function is to explore the search space, following the structure shown in Figure 1.
For more than a decade, researchers like [1-3] have been proposed strategies to optimization of routes applied to Vehicle Optimization Problem based on Genetic Algorithm and its variations.
An adaptive genetic algorithm for solving ill-conditioned equations system is proposed in [60].
"Genetic Algorithm Search for Critical Slip Surface in Multiple-Wedge Stability Analysis." Canadian Geotechnical Journal 36 (2): 382-391.
In this paper, as previously mentioned, a new variant of a genetic algorithm was developed by dynamically adapting some of its parameters (mutation and gap generation).
Genetic algorithm has the following advantages: the object of study is the individual variable, not the decision-making process.

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