Minyang Chen, C. Feng, Ran Cheng · IEEE Transactions on Evolutionary Computation · 2025
As a cornerstone in the Evolutionary Computation (EC) domain, Differential Evolution (DE) is known for its simplicity and effectiveness in handling challenging black-box optimization problems. While the advantages of DE are well-recognized, achieving peak performance heavily depends on its hyperparameters such as the mutation factor, crossover probability, and the selection of specific DE strategies. Traditional approaches to this hyperparameter dilemma have leaned towards parameter tuning or adaptive mechanisms. However, identifying the optimal settings tailored for specific problems remains a persistent challenge. In response, we introduce MetaDE, an approach that evolves DE’s intrinsic hyperparameters and strategies using DE itself at a meta-level. A pivotal aspect of MetaDE is a specialized parameterization technique, which endows it with the capability to dynamically modify DE’s parameters and strategies throughout the evolutionary process. To augment computational efficiency, MetaDE incorporates a design that leverages parallel processing through a GPU-accelerated computing framework. Within such a framework, DE is not just a solver but also an optimizer for its own configurations, thus streamlining the process of hyperparameter optimization and problem-solving into a cohesive and automated workflow. Extensive evaluations on the CEC2022 benchmark suite demonstrate MetaDE’s promising performance. Moreover, when applied to robot control via evolutionary reinforcement learning, MetaDE also demonstrates promising performance. The source code of MetaDE is publicly accessible at: https://github.com/EMI-Group/metade.
Cited by 9Open access
Xin‐She Yang · Elsevier eBooks · 2014
Cited by 14
Jouni Lampinen, Rainer Storn · Studies in fuzziness and soft computing · 2004
Cited by 152
Anyong Qing · 2009
Cited by 104
Ponnuthurai Nagaratnam Suganthan · 2013
tutorial Share on Differential evolution: recent advances Author: Ponnuthurai N. Suganthan Nanyang Technological University, Singapore, Singapore Nanyang Technological University, Singapore, SingaporeView Profile Authors Info & Claims GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computationJuly 2013Pages 845–876https://doi.org/10.1145/2464576.2480811Published:06 July 2013Publication History 0citation206DownloadsMetricsTotal Citations0Total Downloads206Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Cited by 17
Altaf Q. H. Badar · 2021
Cited by 0
Natural computing series · 2005
Cited by 249
Chun‐Wei Tsai, Ming‐Chao Chiang · Elsevier eBooks · 2023
Cited by 6
Valentino Santucci · 2021
The Algebraic Differential Evolution (ADE) is a recently proposed combinatorial evolutionary scheme which mimics the behaviour of the classical Differential Evolution (DE) in discrete search spaces which can be represented as finitely generated groups. ADE has been successfully applied to both permutation and binary optimization problems. However, in the previous works, the relationship between ADE and the classical continuous DE has been only intuitively sketched without any theoretical or experimental proof. Here, we fill this gap by providing both theoretical and experimental justifications proving that ADE is a full-fledged generalization of DE which works across different search spaces. First, we formally prove that there exists a concrete implementation of ADE's algebraic operations converging to the classical vector operations of DE, then we propose a real-vector implementation of ADE and we experimentally prove that its behaviour is statistically equivalent to DE. As conclusion, we also pave the way for further applications of the original DE idea to mixed discrete/continuous search spaces.
Cited by 6
A. Kaveh, Hossein Yousefpoor · Studies in computational intelligence · 2024
Cited by 0
Mousavi, Seyed Muhammad Hossein · 2022
Cited by 0Open access
Diederick Vermetten, Fabio Caraffini, Anna V. Kononova et al. · arXiv (Cornell University) · 2023
New contributions in the field of iterative optimisation heuristics are often made in an iterative manner. Novel algorithmic ideas are not proposed in isolation, but usually as an extension of a preexisting algorithm. Although these contributions are often compared to the base algorithm, it is challenging to make fair comparisons between larger sets of algorithm variants. This happens because even small changes in the experimental setup, parameter settings, or implementation details can cause results to become incomparable. Modular algorithms offer a way to overcome these challenges. By implementing the algorithmic modifications into a common framework, many algorithm variants can be compared, while ensuring that implementation details match in all versions. In this work, we propose a version of a modular framework for the popular Differential Evolution (DE) algorithm. We show that this modular approach not only aids in comparison, but also allows for a much more detailed exploration of the space of possible DE variants. This is illustrated by showing that tuning the settings of modular DE vastly outperforms a set of commonly used DE versions which have been recreated in our framework. We then investigate these tuned algorithms in detail, highlighting the relation between modules and performance on particular problems.
Cited by 0Open access
Aleš Zamuda · IGI Global eBooks · 2016
Cited by 0
Oscar Castillo, Patricia Ochoa, José Soria · SpringerBriefs in applied sciences and technology · 2020
Cited by 9
Yu. L. Dalecky, Sergey Fomin · 1991
Cited by 0
Swagatam Das, Amit Konar, Uday K. Chakraborty · 2007
Differential evolution (DE) has recently emerged as a leading methodology for global search and optimization over continuous, high-dimensional spaces. It has been successfully applied to a wide variety of nearly intractable engineering problems. However, the DE and its variants usually employ a deterministic selection mechanism that always allows the better solution to survive to the next generation. This often prevents DE from escaping local optima at the early stages of search over a multi-modal fitness landscape and leads to a premature convergence. The present work proposes to improve the accuracy and convergence speed of DE by introducing a stochastic selection mechanism. The idea of a conditional acceptance function (that allows accepting inferior solutions with a gradually decaying probability) is borrowed from the realm of the simulated annealing (SA). In addition, the work proposes a center of mass based mutation operator and a decreasing crossover rate in DE. Performance of the resulting hybrid algorithm has been compared with three state-of-the-art adaptive DE schemes. The method is shown to be statistically significantly better on a six-function test-bed and one difficult engineering optimization problem with respect to the following performance measures: solution quality, time to find the solution, frequency of finding the solution, and scalability.
Cited by 34
2008
Abstract — This proof of concept study examines the possibility of specifying the construction of programs using Differential Evolution, and represents a new form of grammar-based genetic programming, Grammatical Differential Evolution (GDE). In GDE each individual member of the population represents a specific choice of program construction rules, where these rules are specified using a Backus-Naur Form grammar. The results demonstrate that it is possible to generate programs using the Grammatical Differential technique.
Cited by 0
Babak Zolghadr‐Asli · 2023
Cited by 2
Rasa Khosrowshahli, Shahryar Rahnamayan · 2023
In order to solve huge-scale optimization problems, many evolutionary algorithms have been proposed. In this paper, we introduce Block Differential Evolution (BDE) algorithm. The BDE can be categorized into the class of memory-efficient optimization algorithms. The main contribution is to solve large and huge-scale problems effectively and efficiently by reducing the problem dimension by utilizing a dimension-blocking scheme. With respect to the block size times reduced memory usage, furthermore, the proposed algorithm is computationally more efficient because DE operations (i.e., mutation, crossover, and selection) are performed on much smaller vector sizes. In fact, the employed blocking approach helps us to map the higher-dimensional problems into a lower-dimensional one which is more convenient to process during the optimization steps. There is a great demanding potential for the proposed approach to be utilized in embedded systems with low computational resources as a compressed optimization algorithm. This strategy is instantiated and evaluated on some well-known benchmark problems and compared with the baseline classic DE algorithm; the reported results are promising and encouraging for conducting further investigations. To the best of our knowledge, that is the first time a variable blocking scheme has been used in any meta-heuristic algorithm. A detailed explanation of the geometrical behavior of the proposed blocking approach is provided which explains how in a higher search space, a blocking approach is meaningful and applicable to accelerate convergence rate while the memory saving is huge. The reported results in this paper show the experiments on Large-Scale Global Optimization Problems proposed in CEC-2013 benchmark suite with 1,000, 10,000, and 100,000 dimensions and clearly are evidence of the possibility of satisfying two conflicting objectives, namely, efficiency and effectiveness, simultaneously. In order to utilize CEC-2013 benchmark problems for the huge dimensions 10,000D and 100,000D, some modifications and expansions have been done. The proposed approach can be utilized in another population-based optimization algorithm (swarm or evolutionary), and it is not restricted to the DE algorithm. In this paper, DE has been used as a parent algorithm for our conducted case study.
Cited by 1
Changshou Deng, Xiaogang Dong, Yucheng Tan et al. · Lecture notes in computer science · 2021
Cited by 1