Genetic algorithm

Choose an initial population of chromosomes; while termination condition not satisfied do repeat if crossover condition satisfied then {select parent chromosomes; choose crossover parameters; perform crossover} if mutation condition satisfied then {select chromosome(s) for mutation; choose mutation points; perform mutation}; evaluate fitness of offspring; until sufficient offspring created; select new population; endwhile

Fig. 1.5. Structure of a GA, extracted from Fig. 2.2, Page 26 in [68].

• GAs are able to find optimal or suboptimal solutions in complex and large search spaces. Moreover, GAs are applicable to nonlinear optimization problems with constraints that can be defined in discrete or continuous search spaces.

• GAs examine many possible solutions at the same time. So there is a higher probability that the search converges to an optimal solution.

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