Since they were proposed as an optimization method, evolutionary algorithms (EA) have been used to solve problems in several research fields. This success is due, besides other things, to the fact that these algorithms do not require previous information regarding the problem to be optimized and offer a high degree of parallelism. However, some problems are computationally intensive regarding the evaluation of each solution, which makes the optimization by EA’s slow in some situations. This chapter proposes a novel EA for numerical optimization inspired by the multiple universes principle of quantum computing that presents faster convergence time for the benchmark problems. Results show that this algorithm can find better solutions, with less evaluations, when compared with similar algorithms, which greatly reduces the convergence time.
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da Cruz, A.V.A., Vellasco, M.M.B.R., Pacheco, M.A.C. (2007). Quantum-Inspired Evolutionary Algorithm for Numerical Optimization. In: Abraham, A., Grosan, C., Ishibuchi, H. (eds) Hybrid Evolutionary Algorithms. Studies in Computational Intelligence, vol 75. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73297-6_2
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