Quantum-Inspired Evolutionary Algorithm for Numerical Optimization

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 considerations regarding the problem to be optimized and offers a high degree of parallelism. However, some problems are computationally intensive regarding solution's evaluation, which makes the optimization by EA's slow for some situations. This paper proposes a novel EA for numerical optimization inspired by the multiple universes principle of quantum computing. Results show that this algorithm can find better solutions, with less evaluations, when compared with similar algorithms.