Population size, genetic algorithms

The size of initial population used in the genetic algorithm was 5 sequences. The size of crossover population was 2 sequences and the mutated population 2 sequences per generation. Consequently the total number of new sequences per generation was 4. The population size after selection was kept in 5. 

The genetic optimization was started with an initial population size of five, which was generated randomly. The algorithm included crossover of two sequences, which were selected randomly. Also random mutations were done on two sequences. The number of mutations per sequence varied from four in the beginning to one in the end per sequence. The steps of the genetic algorithm are ... 

Genetic Algorithms, Noise, and the Sizing of Populations is another fairly descriptive title notice, however, how the use of a triple of topics conveys the breadth of the presentation at the same time that it creates wonder in the reader s mind about how the tliree topics interrelate. Triples can be overused, but they are an effective device if tlie juxtaposition is both informative and interest provoking without being too exotic. 

Conversely, the population size and the number of generations, which are critical for the applicability of a genetic algorithm as a stand-alone optimization meth-... 

Fig. 6.7 Comparison of the maximum of the neural network approximation of the ODHE ethylene yield obtained in 10 runs of the genetic algorithm with a population size 60, and the global maximum obtained with a sequential quadratic programming method run for 15 different starting points.

The convergence speed of the genetic algorithm tends to increase with increasing population size. However, this is merely a general tendency, which interferes with the influence of the remaining adjustable parameters, and only for particular combinations of them becomes really apparent. 

As the noise in the fitness measurement increases, more mutants need to be screened to discover positive mutants. By modeling these effects, Rattray and Shapiro (1997) calculated the optimal population size to achieve the greatest fitness improvement for a genetic algorithm. If Mo is the population size for zero noise, fj is the selection strength, and a is the standard deviation of the noise, then the new population can be scaled as... 

A GA has been implemented to identify the most central groups of nodes of different sizes in the network of Figure 1. Table 2 sinnmarizes the details of the implementation of the GA operators described in Section 3 along with a number of parameters that control the operation of the genetic algorithm such as the population size (i.e., the size of the evolving set of... 

Step 2 The population initialization, randomly generated pop size chromosomes. Since population size has a great influence on the results of genetic algorithm, in order to guarantee the diversity of population and to prevent... 

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