Differential Evolution for Binary Encoding

Gong, Tao; Tuson, Andrew

doi:10.1007/978-3-540-70706-6_24

Cited by 78 publications

(71 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the initial testing stage of this study, the experimental results obtained using this technique for a dynamic environment turned out to be very insufficient. So in the rest of the study and in this paper, the approach explained in [5] by Gong et. al.…”

Section: Binary Differential Evolutionmentioning

confidence: 99%

“…According to the approach defined by Gong et al [5], the multiplication, addition and substitution operators are changed as explained below:…”

Section: Binary Differential Evolutionmentioning

confidence: 99%

“…The value of any parameter in any of the vectors can be either 0 or 1. To keep all values within the set of {0,1}, the result of the subtraction (Xr1,g,i −Xr2,g,i) is obtained using the hamming distance [5] between the two vectors. The result of the addition operator is also calculated using hamming distances.…”

Section: Binary Differential Evolutionmentioning

confidence: 99%

“…For this purpose binary differential evolution (BDE) approaches have been introduced. Two such different implementations can be found in [4] and in [5]. The successful results reported in [3] for applying DE to dynamic optimization problems has been the main motivation behind this study.…”

Section: Introductionmentioning

confidence: 99%

“…A simple bit-matching problem is chosen as the test environment and several experiments are performed using the BDE implementation reported in [5]. The BDE is used in its pure form with no additional measures to adapt it to perform better in dynamic environments.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Experimental analysis of binary differential evolution in dynamic environments

Kanlikilicer

Keleş

Uyar

2007

Proceedings of the 9th Annual Conference Companion on Genetic and Evolutionary Computation

View full text Add to dashboard Cite

Many real-world optimization problems are dynamic in nature. The interest in the Evolutionary Algorithms (EAs) community in applying EA variants to dynamic optimization problems has increased greatly. Differential Evolution (DE) belongs to the group of evolutionary algorithms which operate in continuous search spaces. DE has been successfully applied to many stationary problem domains. Recently there has been some research into applying DE to dynamic optimization problems too. Many real-world problems consist of decision variables which require the optimization algorithm to work with binary parameters. This makes it impossible to apply DE in its basic form. For this purpose, binary differential evolution (BDE) approaches have been introduced. The main focus of this paper is to perform a series of experiments to test the behavior of a simple BDE under different change conditions. A simple bit-matching problem is chosen as the test environment. The results of this preliminary study show that further study is needed to make BDEs suitable to work in dynamic environments.

show abstract

Section: Binary Differential Evolutionmentioning

confidence: 99%

“…According to the approach defined by Gong et al [5], the multiplication, addition and substitution operators are changed as explained below:…”

Section: Binary Differential Evolutionmentioning

confidence: 99%

Section: Binary Differential Evolutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Experimental analysis of binary differential evolution in dynamic environments

Kanlikilicer

Keleş

Uyar

2007

Proceedings of the 9th Annual Conference Companion on Genetic and Evolutionary Computation

View full text Add to dashboard Cite

show abstract

An Improved Binary Differential Evolution Algorithm for Feature Selection in Molecular Signatures

Zhao

Bao

Qiao

et al. 2017

Molecular Informatics

View full text Add to dashboard Cite

The discovery of biomarkers from high-dimensional data is a very challenging task in cancer diagnoses. On the one hand, biomarker discovery is the so-called high-dimensional small-sample problem. On the other hand, these data are redundant and noisy. In recent years, biomarker discovery from high-throughput biological data has become an increasingly important emerging topic in the field of bioinformatics. In this study, we propose a binary differential evolution algorithm for feature selection. Firstly, we suggest using a two-stage approach, where three filter methods including the Fisher score, T-statistics, and Information gain are used to generate the feature pool for input to differential evolution (DE). Secondly, in order to improve the performance of differential evolution algorithm for feature selection, a new variant of binary DE called BDE is proposed. Three optimization strategies are incorporated into the BDE. The first strategy is the heuristic method in initial stage, the second one is the self-adaptive parameter control, and the third one is the minimum change value to improve the exploration behaviour thus enhance the diversity. Finally, Support vector machine (SVM) is used as the classifier in 10 fold cross-validation method. The experimental results of our proposed algorithm on some benchmark datasets demonstrate the effectiveness of our algorithm. In addition, the BDE forged in this study will be of great potential in feature selection problems.

show abstract

Multi-run Differential Evolution Improves the Decomposition of Compound Muscle Action Potential in High-Density Surface Electromyograms

Kramberger

Holobar

2020

8th European Medical and Biological Engineering Conference

View full text Add to dashboard Cite

Differential Evolution for Binary Encoding

Cited by 78 publications

References 5 publications

Experimental analysis of binary differential evolution in dynamic environments

Experimental analysis of binary differential evolution in dynamic environments

An Improved Binary Differential Evolution Algorithm for Feature Selection in Molecular Signatures

Multi-run Differential Evolution Improves the Decomposition of Compound Muscle Action Potential in High-Density Surface Electromyograms

Contact Info

Product

Resources

About