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To monitor the status of neighbouring sections, the partitioned polygon has an ’adjacency graph’ associated with it (Figure 16). Associating a node with each partitioned section and linking it to an adjacent section create the adjacency graph. Figure 16. Adjacency Graph Once a floor plan has been decomposed into a series of rectangular partitions and an adjacency graph constructed, each partition has a genome associated with it. Conceptually, each individual contains a set of genomes: with each genome representing a rectangular partition linked by the adjacency graph.

In the first step a initial population of individuals is created. Usually, the values of the genes of particular individuals are randomly generated. In the next step the individuals’ fitness function value is computed. Szczepanik 58 T. Burczyński selected for the offspring population, which becomes a parent population and the algorithm works iteratively till the end of the computation. g. as the maximum number of iterations. In evolutionary algorithms the floating-point representation is applied, which means that genes included in chromosomes are real numbers.

Section 3: The final section is initialised with randomly selected variables. Unlike BGRID, no effort is made to constrain column positions to realistic spacings. This is to make the GA search for solutions in both the feasible and infeasible regions and hence improve the search. However, the fitness function does penalise individuals that contain a range of column spacings. This is to encourage a degree of uniformity in column spacings, which aids ’buildability’. 14 Evolutionary Operators Genetic algorithms search the solution space by using biologically inspired operators.

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