Genetic initial population

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 ... 

Table 1 The Initial, Random Genetic Algorithm Population (The Significance of the Angles Marked in Bold is Discussed in the Text.)...

These results were obtained by coupling a genetic algorithm for descriptor and calculation parameter (PC, bins) selection to PCA-based partitioning. In these calculations, descriptors were chosen from a pool of approx 150 different ones, and both the number of PCs and bins were allowed to vary from 1 to 15. An initial population of 300 chromosomes was randomly generated with initial bit occupancy of approx 15%. Rates for mutation and crossover operations were set to 5% and 25%, respectively. After PCA-based partitioning, scores were calculated for the following fitness function ... 

GAs are probabilistic search methods based on the mechanics of natural selection and genetics. The basic idea in using a GA as an optimization method is to represent a population of possible solutions in a chromosome-type encoding, called strings, and evaluate these encoded solutions through simulated reproduction, crossover, and mutation to reach an optimal or near-optimal solution. The GA starts with the creation of an initial population of... 

Genetic Algorithm (GA) requires binary coding of possible solutions. In the case of feature selection, elements in the bitstring are set to zero for non-selected variables, while elements representing selected features are set to one. The initial population of bitstrings is selected randomly. All strings are then... 

The origin of life in genetic algorithms happens in a somewhat less romantic fashion than the sudden spark which gave rise to life from the primordial ooze on earth. In a manner not unlike that suggested by the theory of directed panspermia, the implementor of a genetic algorithm seeds the initial population with an appropriate... 

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