Genetic operator generations

Selection alone cannot achieve an optimization towards the solution With mere scicction performed over a number of generations, one would get a population which comprises only the best chromosome of the original population. Therefore, an operator has to be applied which causes variance within the population, This is achieved by the application of genetic operators such as the crossover and the mutation operators. 

Termination Criterion is the criterion in a GA that terminates the genetic operations when either a maximum number of generations has been prodnced or a satisfactory fitness level has been reached for the popnlation. 

Two important issues in GA are the genetic coding used to define the problem and the evaluation function, called the fitness function. Each individual solution in GA is represented with a string called the chromosome. The initial solution population could be generated randomly, which evolves into the next generation by genetic operators, such as selection, crossover and mutation. The solutions coded by strings are evaluated by the fitness function. 

Genetic operators are used to combine existing solutions into others and to generate diversity. The former can be implemented by crossover, and the latter can be implemented by mutation. 

The last genetic operation is selection. Elements of the population are selected to form the next generation with a problem-specific fitness function. Taking the simple example of searching for the structure having the highest boiling, the fitness function can be, for instance, the Wiener index of the structure. 

Genetic operation to generate child population for the next generation... 

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