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genetic operators
Genetic algorithms (GA) are a new type of global optimization methodology based on nature selection and heredity, and its power comes from the evolution process of the population of feasible solutions by using simple genetic operators.
      
We theoretically analyze the impact of the genetic operators on the balance.
      
We also redesign several genetic operators for the decimal code.
      
Genetic operators are fitted for such types of problems to avoid receiving infeasible solutions.
      
The EA selects the MLP initial weights, the learning rate and changes the number of neurons in the hidden layer through the application of specific genetic operators, one of which is BP training.
      
The second process applies the genetic operators (selection, crossover and mutation) on every subpopulation that is so gradually enriched with better individuals.
      
The genetic operators selection and crossover supported by an elitist strategy are used while the diversity of the sub-populations is guaranteed based on implicit mutation.
      
The main idea is to represent truss structures as mathematical graphs and directly apply genetic operators, i.e., mutation and crossover, on them.
      
Besides discussing several specification details in the genetic operators, the model includes four different types of firm forecasting rules and subjects the demand side to serially correlated random shocks.
      
Comparing genetic operators with gaussian mutations in simulated evolutionary processes using linear systems
      
The results indicate that these genetic operators do not compare favorably with more simple random mutation.
      
In previous research we compared mutation and crossover as genetic operators on neural networks directly encoded as real vectors (Manczer and Parisi 1990).
      
In this paper we first summarize such findings and then propose an interpretation in terms of the spatial correlation of the fitness function with respect to the metric defined by the average steps of the genetic operators.
      
Adaptive genetic algorithms, that dynamically adjust selected control parameters or genetic operators during the evolution have been built.
      
We analyzed the strengths and limitations of several well-known genetic operators for TSPs by the experiments.
      
To evaluate these factors, we propose a new genetic algorithm integrating two genetic operators and a heterogeneous pairing selection.
      
The methods work by forming functional approximations of the fitness function which are used to speed up the GA optimization by making the genetic operators more informed.
      
Genetic operators are usually more aggressive when higher alphabets are used.
      
Specific genetic operators are suggested in this paper.
      
This paper proposes a new grammar-guided genetic programming (GGGP) system by introducing two original genetic operators: crossover and mutation, which most influence the evolution process.
      
 

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