Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces

By R. Storn, K. Price. 1997

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Mathematical Optimization

Citations in the corpus

Evolving Robust Neural Architectures to Defend from Adversarial Attacks (2019, methodology): “Specifically, we use the adversarial samples from two types of attacks (L0 and L∞ attacks) with two optimization algorithms (Covariance Matrix Adaptation Evolution Strategy (CMA-ES) [Hansen et al., 2003] and Differential Evolution (DE) [Storn and Price, 1997]).”
A Hybrid Differential Evolution Approach to Designing Deep Convolutional Neural Networks for Image Classification (2018, methodology): “Deferential Evolution (DE) has been proved to be an eﬃcient heuristic for global optimisation over continuous spaces [16], but it has never been used to evolve deep CNNs.”
One Pixel Attack for Fooling Deep Neural Networks (2017, background): “Differential evolution (DE) which belongs to the general class of genetic algorithm (GA), is a solution for solving optimized problems by keeping improving the quality of a candidate population according to a given fitness function [12].”
On the Optimum Synthesis of Four-bar Linkages Using Differential Evolution and the Geometric Centroid of Precision Positions (2002, background): “The weighted difference vector and a randomly chosen mutation vector create a noisy vector, which is subject to crossover process with the target vector in order to generate the trial vector [27,28].”
Modified Grey Wolf Optimizer for Global Engineering Optimization (2016, methodology): “Some of the evolutionary algorithms are Genetic Algorithm (GA) [1], Evolution Strategies (ES) [2], Genetic Programming (GP) [3], Differential Evolution (DE) [4], and Biogeography-Based Optimization (BBO) [5–9].”
Differential Evolution-based Dynamic Economic Dispatch of Generating Units with Valve-point Effects (2008, background): “Storn, R., and Price, K., “Differential evolution—a simple and efficient heuristic for global optimization over continuous space,” J. Global Optim., Vol. 11, pp. 341–359, December 1997.”
Parameter Estimation of the Generalized Extreme Value Distribution for Structural Health Monitoring (2005, methodology): “For the purpose of comparison, differential evolution (DE) [13] is also used to solve Eq.”
Butterfly optimization algorithm: a novel approach for global optimization (2018, methodology): “So considering these factors, seven optimization algorithms have been chosen namely, ABC (Karaboga and Basturk 2007), CS (Yang and Deb 2009), DE (Storn and Price 1997), FA (Yang 2010b), GA (Goldberg and Holland 1988), MBO (Wang et al. 2015) and PSO (Eberhart and Shi 2001).”
Exploring parameter space in reinforcement learning (2010, unknown): “, hill-climbing, differential evolution [23], evolution strategies [15], immunological algorithms, particle swarm optimization [8] or estimation of distribution algorithms [10]), methods from the evolution strategy family seem to have an edge, particularly in cases where the fitness functions are hi”
Real-Time Adversarial Attacks (2019, methodology): “Specifically, we take the differential evolution optimization [Storn and Price, 1997], which was previously used for the “one-pixel” attack [Su et al., 2019] on image recognition systems with similar constraints to our proposed attack.”
DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization (2010, background): “9 [2], [20], [25], [36]; • DE mutation scheme: DE/rand/1/bin [2], [5], [20], [25], [36]; • Value to reach: VTR = 10 − 8 [37], except for f07 of VTR = 10 − 2 ; • Maximum Number of Fitness Function Evaluations (Max NFFEs): For f01, f06, f10, f12, and f13, Max NFFEs = 150000 ; for f03 - f05, Max NFFEs…”
A Brief Review of Nature-Inspired Algorithms for Optimization (2013, methodology): “[69], [71] Atmosphere clouds model Yan and Hao [67] Ant colony optimization Dorigo [15] Biogeography-based optimization Simon [56] Artificial bee colony Karaboga and Basturk [31] Brain Storm Optimization Shi [55] Bacterial foraging Passino [46] Differential evolution Storn and Price [57] Bacterial-G”
Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters (2011, background): “Storn and Price [2] argued that these three control parameters are not difficult to set for obtaining good performance.”