Se propuso QIEA-R (Quantum Inspired Evolutionary Algorithm with Real Codification) para resolver problemas numéricos obteniendo mejores resultados en comparación con los algoritmos tradicionales de EAs, DE y PSO. Se inspira en el concepto de superposición cuántica para reducir el número de evaluaciones. QIEA-R tiene dos pasos importantes: inicialización de la población cuántica y actualización de la población cuántica. Este trabajo analiza estos dos pasos y parámetros relacionados: Tamaño de la población clásica, número de iteraciones, sobre algunas funciones de referencia utilizando mediciones estadísticas para evaluar su importancia y efecto en la calidad de convergencia. Los resultados muestran la importancia del tamaño de la población cuántica y la frecuencia de actualización.

The Evolutionary Algorithms have main features like: population, evolutionary operations (crossover, mate, mutation and others). Most of them are based on randomness and follow a criteria using fitness like selector. The FP-AK-QIEA-R uses probability density function according to best of initial population to sample new population and uses rewarding criteria to sample around the best of every iteration using cumulative density function estimated for Akima interpolation, it was used for mono-objective problems showing good results. The proposal uses the algorithm FP-AK-QIEA-R and add Pareto dominance to experiment with multi-objective problems. The performed experiments use some benchmark functions from the literature and initial results shows a promissory way for the algorithm.

There are many Evolutionary Algorithms which main features are: population, evolutionary operations (crossover, mate, mutation and others). Most of them are based on randomness and follow a criteria using fitness like selector. The proposal uses probability density function according to best of initial population to sample new population and save better individuals iteratively. Then using centroid criteria sample for every dimension and get better individuals. It had good results with benchmark functions. A real application was performed with experiments in protein folding and it showed good results.