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Control Parameters in Differential Evolution (DE): A Short Review

Ali Wagdy Mohamed
2018 Robotics & Automation Engineering Journal  
Differential Evolution (DE) is a population based stochastic search algorithm for optimization. DE has three main control parameters, Crossover (cr), Mutation factor (F) and Population size (NP).  ...  These control parameters play a vital and crucial rule in improving the performance of search process in DE. This paper introduces a brief review for control parameters in Differential evolution (DE).  ...  Large population size increases the diversity but consumes more resources (function calls), while small population size may cause stagnation or tripping in local optima.  ... 
doi:10.19080/raej.2018.03.555607 fatcat:hbdk72fkm5bp3bnm6vcnfrffdm

Experimental evolution reveals natural selection on standing genetic variation

Henrique Teotónio, Ivo M Chelo, Martina Bradić, Michael R Rose, Anthony D Long
2009 Nature Genetics  
We carry out 50 generations of experimental reverse evolution in populations of Drosophila melanogaster, previously differentiated by forward evolution, and follow changes in the frequency of SNPs in both  ...  During the 50 generations of the reverse-evolution experiment, the SNP frequencies in control populations did not seem to have changed (Fig. 2a) .  ...  size experienced by all the populations in the ancestral environment.  ... 
doi:10.1038/ng.289 pmid:19136954 fatcat:pvdftxo2cvg2pdbfscvh4wd5va

Truss structure optimization using adaptive multi-population differential evolution

Chun-Yin Wu, Ko-Ying Tseng
2010 Structural And Multidisciplinary Optimization  
This paper applies multi-population differential evolution (MPDE) with a penalty-based, self-adaptive strategy-the adaptive multi-population differential evolution (AMPDE)-to solve truss optimization problems  ...  The self-adaptive strategy developed in this study is a new adaptive approach that adjusts the control parameters of MPDE by monitoring the number of infeasible solutions generated during the evolution  ...  The multi-population mechanism is introduced in DE to maintain the diversity of the search space.  ... 
doi:10.1007/s00158-010-0507-9 fatcat:colvjsdu2bfz7navct4st5vqje

Covariance Matrix Learning Differential Evolution Algorithm Based on Correlation

Sainan Yuan, Quanxi Feng
2021 International Journal of Intelligence Science  
Differential evolution algorithm based on the covariance matrix learning can adjust the coordinate system according to the characteristics of the population, which makes the search move in a more favorable  ...  First, a hybrid mutation strategy is designed to balance the diversity and convergence of the population; secondly, the covariance learning matrix is constructed by selecting the individual with the less  ...  This work is proudly supported in part by National Natural Science Foundation of China (No. 61763008, 11661030, 11401357, Conflicts of Interest The authors declare no conflicts of interest regarding  ... 
doi:10.4236/ijis.2021.111002 fatcat:nm7wjwnu55gbdnhiuuaczquplq

Adaptive differential evolution: A visual comparison

Chi-An Chen, Tsung-Che Chiang
2015 2015 IEEE Congress on Evolutionary Computation (CEC)  
F4).  A good control mechanism should consider population diversity (F4).  ...  Wu, "An improved self-adaptive control parameter of differential evolution for global optimization," In Z. Cai et al. (Eds.)  ...  Chen & Chiang, Adaptive Differential Evolution: A Visual Comparison Future Work  We need to make observations to more functions and draw general conclusions.  We will also do investigations on recent  ... 
doi:10.1109/cec.2015.7256918 dblp:conf/cec/ChenC15 fatcat:gkmk45wlzneovjxjtsdlzze4ce

Population divergence in plant species reflects latitudinal biodiversity gradients

S. H. Eo, J. P Wares, J. P Carroll
2008 Biology Letters  
In this meta-analysis, we cannot distinguish whether higher divergence is due to lower gene flow or smaller population size, or whether this pattern is driven by faster molecular evolution or a longer  ...  Such causal mechanisms should be assessed using well-supported phylogeographic data by DNA sequences representing each population, in conjunction with population dynamics and ecological data, based on  ... 
doi:10.1098/rsbl.2008.0109 pmid:18492649 pmcid:PMC2610133 fatcat:2wytvkg47jgm5msbnhlnxbn4l4

Insights from Population Genomics to Enhance and Sustain Biological Control of Insect Pests

Arun Sethuraman, Fredric J. Janzen, David W. Weisrock, John J. Obrycki
2020 Insects  
Recent advances in genomic sequencing technology and model-based statistical methods to analyze population-scale genomic data provide a much needed impetus for biological control programs to benefit by  ...  Particularly, population genomics presents exceptional opportunities to study adaptive evolution and invasiveness of pests and biological control organisms.  ...  Natural Selection and Evolution Populations of biological control organisms in new environments, apart from undergoing population size change, are also subject to adaptive evolution in response to selection  ... 
doi:10.3390/insects11080462 pmid:32708047 pmcid:PMC7469154 fatcat:rg7r2d7ckzdkpdjk46e3quaegm

Evolution of genome-phenome diversity under environmental stress

E. Nevo
2001 Proceedings of the National Academy of Sciences of the United States of America  
(i) How much of the genomic and phenomic diversity in nature is adaptive and processed by natural selection?  ...  Biodiversity evolution, even in small isolated populations, is primarily driven by natural selection, including diversifying, balancing, cyclical, and purifying selective regimes, interacting with, but  ...  Evidence Allozyme Diversity and Evolution. Darwin introduced natural selection as the major mechanism of evolution.  ... 
doi:10.1073/pnas.101109298 pmid:11371642 pmcid:PMC33451 fatcat:2pmolxb6afaaxnsvqyews5o3de

Editorial scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems

M. Lozano, D. Molina, F. Herrera
2010 Soft Computing - A Fusion of Foundations, Methodologies and Applications  
Some examples are neural networks optimization, aerospace design, biotechnology, control, economic, signal processing, microware, industrial electronics, industrial engineering, water resources management  ...  In addition, it provides the link to an associated Website where complementary material to the special issue is available.  ...  The paper by Brest and Maucˇec proposes a selfadaptive differential evolution algorithm with population size reduction for solving large-scale optimization problems with continuous variables.  ... 
doi:10.1007/s00500-010-0639-2 fatcat:2ycwf2nn4ze5zpuhc2374khvui

Research on an Improved Differential Evolution Algorithm based on Three Strategies for Solving Complex Function

Hao Jia
2015 International Journal of Smart Home  
ability and the stability of optimization, an improved differential evolution algorithm based on multi-population and dynamic local search(MPDLSDE) is proposed in this paper.  ...  For the shortcomings of differential evolution algorithm (DE), such as the low convergence rate in the late evolution, easy to trap into the local optimal solution, and weak situation of the global search  ...  [29] proposed a novel differential evolution algorithm based on adaptive differential evolution algorithm by implementing pbest roulette wheel selection and retention mechanism.  ... 
doi:10.14257/ijsh.2015.9.11.30 fatcat:pzhf62i2dncgjnz5hho4qv2edm

An Improved Adaptive Differential Evolution based on Hybrid Method for Function Optimization

Li Huang
2016 International Journal of Hybrid Information Technology  
In order to improve the optimum speed, crease the diversity of the population and overcome the premature convergence problem in differential evolution(DE) algorithm for solving the complex optimization  ...  The multi-population with parallel evolution is used to preserve the diversity of the population at the initial generation.  ...  Introduction Differential evolution (DE) algorithm is a branch of genetic algorithm, it is proposed by Storn in 1995 [1] .  ... 
doi:10.14257/ijhit.2016.9.3.09 fatcat:e5pis4bq2zcxlplmwzl62ntzne

Evolution of life in urban environments

Marc T. J. Johnson, Jason Munshi-South
2017 Science  
These environmental changes brought by global urbanization are creating novel ecosystems with unknown consequences for the evolution of life.  ...  Urban areas also host more non-native species and reduced abundance and diversity of many native species.  ...  Yadav for permission to use the photo in the summary, B. Cohan for the artwork in Fig. 2  ... 
doi:10.1126/science.aam8327 pmid:29097520 fatcat:evyyi5mu2fbv5m26lowcbwi46i

Nucleotide diversity inflation as a genome-wide response to experimental lifespan extension in Drosophila melanogaster

Pawel Michalak, Lin Kang, Pernille M. Sarup, Mads F. Schou, Volker Loeschcke
2017 BMC Genomics  
Alternatively, genetic diversity may increase as a result of positive frequency-dependent selection and genetic purging in bottlenecked populations.  ...  melanogaster leads to an extensive genome-wide increase of nucleotide diversity in the selected lines compared to replicate control lines, pronounced in regions with no or low recombination, such as chromosome  ...  Sequencing was done by BGI. Funding The Danish Natural Sciences Research council and the Carlsberg Foundation for financial support to VL.  ... 
doi:10.1186/s12864-017-3485-0 pmid:28088192 pmcid:PMC5237518 fatcat:5ex3cqgpzbdbhihyorhvzcgfbm

Differential evolution and non-separability

Andrew M. Sutton, Monte Lunacek, L. Darrell Whitley
2007 Proceedings of the 9th annual conference on Genetic and evolutionary computation - GECCO '07  
Recent results show that the Differential Evolution algorithm has significant difficulty on functions that are not linearly separable.  ...  We find that imposing pressure in the form of rankbased differential mutation results in a significant improvement of exploitation on rotated benchmarks.  ...  Diversity might also be injected by using a soft restart mechanism [5] . The rank-based variant, unlike the classic DE algorithm, calls for the population to be sorted in each generation.  ... 
doi:10.1145/1276958.1277221 dblp:conf/gecco/SuttonLW07 fatcat:yr7sesb67nh6dct76mpdu2mh6i

A collaborative model for tracking optima in dynamic environments

Rodica Ioana Lung, D. Dumitrescu
2007 2007 IEEE Congress on Evolutionary Computation  
CESO is a simple method for tracking moving optima in a dynamic environment by combining the search abilities of an evolutionary algorithm for multimodal optimization and a particle swarm optimization  ...  A new hybrid approach to optimization in dynamic environments called Collaborative Evolutionary-Swarm Optimization (CESO) is presented.  ...  CROWDING BASED DIFFERENTIAL EVOLUTION The first population used by CESO, called the CRDE population is evolved by an evolutionary multimodal optimization algorithm in order to maintain a good population  ... 
doi:10.1109/cec.2007.4424520 dblp:conf/cec/LungD07 fatcat:5sssyavmr5aqxa472qalrqahi4
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