The hybrid of Differential Evolution algorithm with Kalman Filtering and Bacterial Foraging algorithm is a novel global optimization method that is implemented in this research to obtain the best kinetic parameter value. The proposed algorithm is then used to model tyrosine production in mus musculus (mouse) by using a dataset, JAK/STAT (Janus Kinase Signal Transducer and Activator of Transcrip...

A Parallel Differential Evolution algorithm is presented in this work, developed for a cluster of computers in Windows environment. The parallelization is realized using an asynchronous approach, utilizing a Master-Slave architecture. A separate executable program is used to evolve each member of the population. The current population is stored in a folder accessible by all executables; each cu...

The problem of optimal non-hierarchical clustering is addressed. A new algorithm combining differential evolution and k-means is proposed and tested on eight well-known real-world data sets. Two criteria (clustering validity indexes), namely TRW and VCR, were used in the optimization of classification. The classification of objects to be optimized is encoded by the cluster centers in differenti...

This article discusses the stagnation of an evolutionary optimization algorithm called Differential Evolution. Stagnation problem refers to a situation in which the optimum seeking process stagnates before finding a globally optimal solution. Typically, stagnation occurs virtually without any obvious reason. The stagnation differs from the premature convergence so that the population remains di...

One of the essential motivations for feature selection is to defeat the curse of dimensionality problem. Feature selection optimization is nothing but generating best feature subset with maximum relevance, which improves the result of classification accuracy in pattern recognition. In this research work, Differential Evolution and Genetic Algorithm, the two population based feature selection me...

differential evolution Iztok Fister Jr.,∗ Iztok Fister,† and Janez Brest‡ Abstract Differential evolution possesses a multitude of various strategies for generating new trial solutions. Unfortunately, the best strategy is not known in advance. Moreover, this strategy usually depends on the problem to be solved. This paper suggests using various regression methods (like random forest, extremely ...

Differential evolution (DE) algorithms have been extensively and frequently applied to solve optimizationproblems. Theoretical analyses of their properties are important to understand the underlying mechanismsand to develop more efficient algorithms. In this paper, firstly, we introduce an absorbing Markovsequence to model a DE algorithm. Secondly, we propose and prove two theorems that provide...

In this paper a quantum inspired differential evolution algorithm (QDEA) for image registration is presented. Image registration is a fundamental task in almost every computer vision system. It aims to find the best transformation that allows the superimposing of the common parts of two images. The proposed algorithm is a novel hybridization between differential evolution algorithms and quantum...

In this paper, we present a new method for the design of multi-band microstrip filters. The proposed design method is based on Differential Evolution (DE) with strategy adaptation. This selfadaptive DE (SaDE) uses previous experience in both trial vector generation strategies and control parameter tuning. We apply this algorithm to two design cases of dual and tri-band filters for WiFi and WiMa...

The main objective of this research is to automatically design Artificial Neural Network models with sigmoid basis units for multiclassification tasks in predictive microbiology. The classifiers obtained achieve a double objective: a high classification level in the dataset and high classification levels for each class. The Memetic Pareto Differential Evolution Neural Network chosen to learn th...