An Improved Evolutionary Algorithm for Reducing the Number of Function Evaluations
Erik Cuevas, Eduardo L. Santuario, Daniel Zaldívar, Marco Pérez‐Cisneros · Intelligent Automation & Soft Computing · 2015
AbstractMany engineering applications can be approached as optimization problems whose solution commonly involves the execution of computational expensive objective functions. Recently, Evolutionary Algorithms (EAs) are gaining popularity for solving complex problems that are encountered in many disciplines, delivering a more robust and effective way to locate global optima in comparison to classical optimization methods. However, applying EA’s to real-world problems demands a large number of function evaluations before delivering a satisfying result. Under such circumstances, several EAs have been adapted to reduce the number of function evaluations by using alternative models to substitute the original objective function. Despite such approaches employ a reduced number of function evaluations, the use of alternative models seriously affects their original EA search capacities and their solution accuracy. Recently, a new evolutionary method called the Adaptive Population with Reduced Evaluations (APRE) h...