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Improving differential evolution through a unified approach

机译：通过统一的方法改善差异发展

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Only a few attempts in past have been made in adopting a unified outlook towards different paradigms in evolutionary computation (EC). The underlying motivation of these studies was aimed at gaining better understanding of evolutionary methods, both at the level of theory as well as application, in order to design efficient evolutionary algorithms for solving wide-range of complex problems. However, the past descriptions have either been too general or sometimes in issuing a clear direction for improving an evolutionary paradigm for a task-specific. This paper recollects the 'Unified Theory of Evolutionary Computation' from past and investigates four steps-Initialization, Selection, Generation and Replacement, which are sufficient to describe traditional forms of Evolutionary Optimization Systems such as Genetic Algorithms, Evolutionary Strategies, Evolutionary Programming, Particle Swarm Optimization and differential evolution (DE). Then, a relatively new evolutionary paradigm, DE, is chosen and studied for its performance on a set of unimodal problems. Discovering DEs inability as an efficient solver, DE is reviewed under 'Unified Framework' and functional requirements of each step are evaluated. Targeted towards enhancing the DE's performance, several modifications are proposed through borrowing of operations from a benchmark solver G3-PCX. Success of this exercise is demonstrated in a step-by-step fashion via simulation results. The Unified Approach is highly helpful in understanding the role and re-modeling of DE steps in order to efficiently solve unimodal problems. In an avalanching-age of new methods in EC, this study outlines a direction for advancing EC methods by undertaking a collective outlook and an approach of concept-sharing.
机译：过去，在对演化计算（EC）的不同范式采取统一观点方面仅作了几次尝试。这些研究的基本动机旨在在理论和应用层面上更好地理解进化方法，从而设计出解决广泛复杂问题的有效进化算法。但是，过去的描述要么太笼统，要么有时在发布明确的方向以改进针对特定任务的进化范例。本文回顾了过去的“进化计算统一理论”，并研究了四个步骤：初始化，选择，生成和替换，这些步骤足以描述传统形式的进化优化系统，例如遗传算法，进化策略，进化规划，粒子群。优化和差分进化（DE）。然后，选择并研究了相对较新的进化范式DE，以研究其在一系列单峰问题上的性能。发现DE不能作为有效的求解器，在“统一框架”下审查DE，并评估每个步骤的功能要求。为了提高DE的性能，通过从基准求解器G3-PCX借用操作，提出了一些修改方案。通过模拟结果逐步演示了此练习的成功。统一方法对于理解DE步骤的作用和重新建模以有效解决单峰问题非常有帮助。在EC中新方法的泛滥时代，这项研究概述了通过采取集体观点和概念共享方法来推进EC方法的方向。

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