A Differential Evolution (DE) is introduced to predict the parameters of the soil water retention curve (SWRC) and it is configured for reliability and efficiency with the Unsaturated Soil Hydraulic Property Database (UNSODA). The main investigated dataset is 235 samples from lab_drying_h-t table and the testing shows that the data resource is reliable and steady. Some specific statistical comp...

iii

A new version of differential evolution (DE) algorithm, in which immune concepts and methods are applied to determine the parameter setting, named immune self-adaptive differential evolution (ISDE), is proposed to improve the performance of the DE algorithm. During the actual operation, ISDE seeks the optimal parameters arising from the evolutionary process, which enable ISDE to alter the algor...

Back propagation neural network is successfully used in various fields, particularly in pattern recognition. Despite numerous applications, back propagation neural network`s design and optimization are developed by trial-and-error process, which is time-consuming. On the other hand, although a dataset may contain many features, these features may not be useful in a back propagation neural netwo...

This paper presents an improved Differential Evolution algorithm (IDE). It is aimed at improving its performance in estimating the relevant parameters for metabolic pathway data to simulate glycolysis pathway for yeast. Metabolic pathway data are expected to be of significant help in the development of efficient tools in kinetic modeling and parameter estimation platforms. Nonetheless, due to t...

Different constraint handling techniques have been used with multiobjective evolutionary algorithms (MOEA) to solve constrained multiobjective optimization problems. It is impossible for a single constraint handling technique to outperform all other constraint handling techniques always on every problem irrespective of the exhaustiveness of parameter tuning. To overcome this selection problem, ...

Natural selection is the central concept of Darwinian evolution and hence selection is central for evolutionary computation. Naive models of evolution define natural selection as a process which brings in differential reproductive capabilities in organisms of a population, and hence, evolutionary algorithms implement selection by differential reproduction: the fittest members of the population ...

—This paper introduces an improved Differential Evolution algorithm (IDE) which aims at improving its performance in estimating the relevant parameters for metabolic pathway data to simulate glycolysis pathway for yeast. Metabolic pathway data are expected to be of significant help in the development of efficient tools in kinetic modeling and parameter estimation platforms. Many computation alg...

The Distributed Differential Evolution (dDE) algorithm is a natural extension of the Differential Evolution (DE) algorithm, which is a recent addition to the Evolutionary Algorithms (EAs) pool, in the Evolutionary Computing (EC) field of computer science. The algorithmic novelty of the dDE algorithm is well evident in the literature. However, the theoretical studies on the performance of the dD...

Appropriately adapting mutation strategies is a challengeable problem of the literature of the Differential Evolution (DE). The Strategy adaptation Mechanism (SaM) can convert a control parameter adaptation algorithm to a strategy adaptation algorithm. To improve the quality of optimization result, the exploration property is important in the early stage of optimization process and the exploita...