Dynamic Fitness Landscape Analysis on Differential Evolution Algorithm

2016 ◽  
pp. 179-184 ◽  
Author(s):  
Shuling Yang ◽  
Kangshun Li ◽  
Wei Li ◽  
Weiguang Chen ◽  
Yan Chen
Keyword(s):  
Differential Evolution ◽  
Fitness Landscape ◽  
Differential Evolution Algorithm ◽  
Landscape Analysis ◽  
Fitness Landscape Analysis ◽  
Evolution Algorithm

scholarly journals A multi-model assisted differential evolution algorithm for computationally expensive optimization problems

2021 ◽  
Author(s):  
Haibo Yu ◽  
Li Kang ◽  
Ying Tan ◽  
Jianchao Zeng ◽  
Chaoli Sun
Keyword(s):  
Differential Evolution ◽  
Basis Function ◽  
Fitness Landscape ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Expensive Optimization Problems ◽  
Evolution Algorithm ◽  
Computationally Expensive ◽  
Computational Budget ◽  
Expensive Optimization

AbstractSurrogate models are commonly used to reduce the number of required expensive fitness evaluations in optimizing computationally expensive problems. Although many competitive surrogate-assisted evolutionary algorithms have been proposed, it remains a challenging issue to develop an effective model management strategy to address problems with different landscape features under a limited computational budget. This paper adopts a coarse-to-fine evaluation scheme basing on two surrogate models, i.e., a coarse Gaussian process and a fine radial basis function, for assisting a differential evolution algorithm to solve computationally expensive optimization problems. The coarse Gaussian process model is meant to capture the general contour of the fitness landscape to estimate the fitness and its degree of uncertainty. A surrogate-assisted environmental selection strategy is then developed according to the non-dominance relationship between approximated fitness and estimated uncertainty. Meanwhile, the fine radial basis function model aims to learn the details of the local fitness landscape to refine the approximation quality of the new parent population and find the local optima for real-evaluations. The performance and scalability of the proposed method are extensively evaluated on two sets of widely used benchmark problems. Experimental results show that the proposed method can outperform several state-of-the-art algorithms within a limited computational budget.

scholarly journals A self-feedback strategy differential evolution with fitness landscape analysis

2018 ◽  
Vol 22 (23) ◽  
pp. 7773-7785 ◽  
Author(s):  
Ying Huang ◽  
Wei Li ◽  
Chengtian Ouyang ◽  
Yan Chen
Keyword(s):  
Differential Evolution ◽  
Fitness Landscape ◽  
Landscape Analysis ◽  
Feedback Strategy ◽  
Fitness Landscape Analysis

Performance Analyses of Differential Evolution Algorithm Based on Dynamic Fitness Landscape

2019 ◽  
Vol 13 (1) ◽  
pp. 36-61 ◽  
Author(s):  
Kangshun Li ◽  
Zhuozhi Liang ◽  
Shuling Yang ◽  
Zhangxing Chen ◽  
Hui Wang ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Fitness Landscape ◽  
Optimization Problems ◽  
Dynamic Properties ◽  
Differential Evolution Algorithm ◽  
Global Optimum ◽  
De Algorithm ◽  
Evolution Algorithm ◽  
Fitness Distance Correlation ◽  
And Performance

Dynamic fitness landscape analyses contain different metrics to attempt to analyze optimization problems. In this article, some of dynamic fitness landscape metrics are selected to discuss differential evolution (DE) algorithm properties and performance. Based on traditional differential evolution algorithm, benchmark functions and dynamic fitness landscape measures such as fitness distance correlation for calculating the distance to the nearest global optimum, ruggedness based on entropy, dynamic severity for estimating dynamic properties, a fitness cloud for getting a visual rendering of evolvability and a gradient for analyzing micro changes of benchmark functions in differential evolution algorithm, the authors obtain useful results and try to apply effective data, figures and graphs to analyze the performance differential evolution algorithm and make conclusions. Those metrics have great value and more details as DE performance.

Modified differential evolution algorithm for semi-supervised fuzzy clustering

2009 ◽  
Vol 29 (4) ◽  
pp. 1046-1047
Author(s):  
Song-shun ZHANG ◽  
Chao-feng LI ◽  
Xiao-jun WU ◽  
Cui-fang GAO
Keyword(s):  
Differential Evolution ◽  
Fuzzy Clustering ◽  
Differential Evolution Algorithm ◽  
Evolution Algorithm
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