Investigating Smart Sampling as a population initialization method for Differential Evolution in continuous problems

Recently, researches have shown that the performance of metaheuristics can be affected by population initialization. Opposition-based Differential Evolution (ODE), Quasi-Oppositional Differential Evolution (QODE), and Uniform-Quasi-Opposition Differential Evolution (UQODE) are three state-of-the-art methods that improve the performance of the Differential Evolution algorithm based on population initialization and different search strategies. In a different approach to achieve similar results, this paper presents a technique to discover promising regions in a continuous search-space of an optimization problem. Using machine-learning techniques, the algorithm named Smart Sampling (SS) finds regions with high possibility of containing a global optimum. Next, a metaheuristic can be initialized inside each region to find that optimum. SS and DE were combined (originating the SSDE algorithm) to evaluate our approach, and experiments were conducted in the same set of benchmark functions used by ODE, QODE and UQODE authors. Results have shown that the total number of function evaluations required by DE to reach the global optimum can be significantly reduced and that the success rate improves if SS is employed first. Such results are also in consonance with results from the literature, stating the importance of an adequate starting population. Moreover, SS presents better efficacy to find initial populations of superior quality when compared to the other three algorithms that employ oppositional learning. Finally and most important, the SS performance in finding promising regions is independent of the employed metaheuristic with which SS is combined, making SS suitable to improve the performance of a large variety of optimization techniques.


  • A hybrid global optimization algorithm for unconstrained continuous global optimization
  • Works for black-box optimization
  • Uses machine learning techniques (classification) for efficient global optimization
  • An improved algorithm that searches for promising regions
  • Avoid local optima
  • Results in a more efficient global optimization method
  • Tested in: schwefel, rastrigin, griewank, ackley, levy, michalewicz, zakharov, step, alpine, exponential, salomon
  • Compared to: differential evolution, opposition-based differential evolution


author = {Melo, Vin\’{\i}cius Veloso de and Delbem, Alexandre Cl\’{a}udio Botazzo},
title = {Investigating Smart Sampling as a population initialization method for Differential Evolution in continuous problems},
journal = {Inf. Sci.},
issue_date = {June, 2012},
volume = {193},
month = jun,
year = {2012},
issn = {0020-0255},
pages = {36–53},
numpages = {18},
url = {http://dx.doi.org/10.1016/j.ins.2011.12.037},
doi = {10.1016/j.ins.2011.12.037},
acmid = {2160215},
publisher = {Elsevier Science Inc.},
address = {New York, NY, USA},
keywords = {Differential Evolution, Global Optimization, Metaheuristic, Population initialization, Promising region, Smart Sampling},

Evolució diferencial. Optimització global. Metaheuristic. Inicialització de la població. Regió prometedor. Mostreig intel ligent. Algorisme evolutiva.
Évolution différentielle. Optimisation globale. Métaheuristique. Initialisation de la population. Région prometteuse. Smart d’échantillonnage. Algorithme évolutionnaire.
Differenzielle Evolution. Globale Optimierung. Metaheuristic. Bevölkerung-Initialisierung. Viel versprechende Region. Intelligent Sampling. Evolutionärer Algorithmus.
विभेदक विकास। वैश्विक अनुकूलन। Metaheuristic. जनसंख्या का प्रारंभ। आशाजनक क्षेत्र। स्मार्ट नमूना। विकासवादी एल्गोरिथ्म।
Evolusi yang berbeza. Global pengoptimuman. Metaheuristic. Pengawalan populasi. Rantau yang memberangsangkan. Smart persampelan. Evolusi algoritma.
Evolución diferencial. Optimización global. Metaheurísticas. Inicialización de la población. Región prometedora. Muestreo inteligente. Algoritmo evolutivo.