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An Improved Evolutionary Algorithm for Reducing the Number of Function Evaluations

机译：减少功能求值次数的改进进化算法

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摘要

Many 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) has been proposed to solve several image processing problems. APRE reduces the number of function evaluations through the use of two mechanisms: ( 1) The dynamic adaptation of the population and ( 2) the incorporation of a fitness calculation strategy, which decides when it is feasible to calculate or only estimate new generated individuals. As a result, the approach can substantially reduce the number of function evaluations, yet preserving the good search capabilities of an evolutionary approach. In this paper, the performance of APRE as a global optimization algorithm is presented. In order to illustrate the proficiency and robustness of APRE, it has been compared to other approaches that have been previously conceived to reduce the number of function evaluations. The comparison examines several standard benchmark functions, which are commonly considered within the EA field. Conducted simulations have confirmed that the proposed method achieves the best balance over its counterparts, in terms of the number of function evaluations and the solution accuracy.
机译：可以将许多工程应用程序视为优化问题，其解决方案通常涉及执行计算上昂贵的目标函数。近年来，进化算法（EA）在解决许多学科中遇到的复杂问题方面变得越来越流行，与经典的优化方法相比，它提供了一种更强大，更有效的方法来定位全局最优。但是，将EA应用于实际问题需要先进行大量功能评估，然后才能得出令人满意的结果。在这种情况下，通过使用替代模型替代原始目标函数，已对几种EA进行了调整，以减少函数评估的次数。尽管这种方法减少了功能评估的次数，但是使用替代模型会严重影响其原始EA搜索功能及其解决方案的准确性。近来，已经提出了一种新的进化方法，称为减少估计的自适应种群（APRE），以解决若干图像处理问题。 APRE通过使用两种机制减少了功能评估的次数：（1）群体的动态适应；（2）引入适应性计算策略，该策略决定何时计算或仅估计新生成的个体。结果，该方法可以大大减少功能评估的次数，同时保留进化方法的良好搜索能力。本文介绍了APRE作为全局优化算法的性能。为了说明APRE的熟练程度和鲁棒性，已将其与以前设想的减少功能评估次数的其他方法进行了比较。比较检查了几种标准基准功能，这些功能通常在EA领域内考虑。进行的仿真已经证实，就功能评估的数量和求解精度而言，所提出的方法在同类方法之间达到了最佳平衡。

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