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4) We repeat steps 1−3 for z2 , z3 and z4 . 5) We next consider the possibility of mutation for each bit in each temporary individual z1 , z2 , z3 and z4 . 4. 6) Now that we have a modified population of {zk } individuals, we copy zk to xk for k ∈ [1,4], and the first BBO generation is complete. The above process continues until some convergence criterion is met. For instance, we could continue for a specified number of generations, or continue until we achieve a satisfactory fitness value, or continue until the fitness value stops changing.

Problem set descriptions. 5) and SPSO 07 performs best on problem P04. 3, SGA, ADE and BBO, all attain the same optimum. These results indicate that BBO performs similarly to ADE, and is significantly better than SGA and SPSO 07. 5. Comparison of real-world optimization results for SGA, SPSO 07, ADE and BBO. The best result in each row is shown in bold BBO Extensions 59 If we use more advanced versions of GA, PSO and DE, it might be possible to obtain better results than those here. However, the same could be said for other improvements of BBO.

It also makes high HSI solutions likely to mutate, which gives them a chance of improving even more than they already have. We usually use elitism, which is a common EA mechanism, to save the SIVs of the habitat that has the best solution in the optimization process, so even if mutation ruins its HSI, we have saved it and can revert back to it if needed. So we use mutation on both poor solutions and good solutions. Those solutions that are average are hopefully improving already, and so we avoid mutating them, although there is still some mutation probability, except for the most probable solution.

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