Graph Partitioning (ISTE) by

Language: eng
Format: azw3
ISBN: 9781118601259
Publisher: Wiley
Published: 2013-01-23T16:00:00+00:00

7.2.1.1. Algorithm

Algorithm 7.1 shows the generic stages of a genetic algorithm. In such an algorithm, the population evolves iteratively. The main stages of this evolution are the following:

1) an initial population of nb_chromosomes is created. All the future generations are derived from this population. It is therefore better that this initial population covers a large part of the solution space of the problem; this is why it is often created randomly;

2) population evolves into successive generation through an iterative loop, with respect to three principles:

– a chromosome adaptation corresponds to the fitness value of this chromosome relative to the others;

– chromosomes selections form the population in a two stage process. First the future parents are selected, meaning the chromosomes which will be reproduced. This does not mean that, in this first stage, the other chromosomes disappear, since this is the subject of the second stage. This first stage is carried out by the selection operator. In the second stage, the chromosomes, which will form the new population, are selected. This selection is carried out by the replacement operator;

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