Finding the Global Energy Minimum Evolutionary Algorithms and Simulated Annealing

9 Finding the Global Energy Minimum Evolutionary Algorithms and Simulated Annealing 

20 The chromosome in a genetic algorithm codes for the torsion angles of the rotatable bonds 

Fig 9 21 The basis of roulette wheel selection shmoing how the more fit members of the population are selec ted in proportion to their fitness values 

The crossover operator is applied to the selected pairs of parents with a probability a typical value being 0.8 (i.e. there is an 80% chance that any of the p/2 pairs will actually undergo this type of recombination). Following the crossover phase mutation is applied to all individuals in the population. Here, each bit may be inverted (0 to 1 and vice versa) with a probability Pu,. The mutahon operator is usually assigned a low probability (e.g. 0.01). 

This completes one complete cycle of the genetic algorithm. The new population then becomes the current population ready for a new cycle. The algorithm repeatedly applies this sequence for a predetermined number of iterations and/or until it converges 

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