Selection by Parts: ‘Selection in Two Episodes’ in Evolutionary Algorithms
نویسنده
چکیده
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 are reproduced preferentially at the expense of the less fit members of the population. Formal models in evolutionary biology often subdivide selection into components called ‘episodes of selection’ to capture the different complex mechanisms of nature by which Darwinian evolution can occur. In this paper we introduce the concept of ‘episodes of selection’ in evolutionary computation by means of “A Conceptual Evolutionary model”(ACE-model). This model captures selection in two episodes in two phases of evolutionary cycle. Here we give formal description of ACE-model, in which one can mechanize the two phases in different possible ways. To demonstrate the concept of episodes of selection we propose evolutionary algorithms based on ACE-model, by giving simple mechanisms for implementation of two phases. Finally we discuss the importance of introducing episodes of selection in evolutionary algorithms by simulations of proposed evolutionary algorithms for function optimization.
منابع مشابه
Improving of Feature Selection in Speech Emotion Recognition Based-on Hybrid Evolutionary Algorithms
One of the important issues in speech emotion recognizing is selecting of appropriate feature sets in order to improve the detection rate and classification accuracy. In last studies researchers tried to select the appropriate features for classification by using the selecting and reducing the space of features methods, such as the Fisher and PCA. In this research, a hybrid evolutionary algorit...
متن کاملComparison of particle swarm optimization and tabu search algorithms for portfolio selection problem
Using Metaheuristics models and Evolutionary Algorithms for solving portfolio problem has been considered in recent years.In this study, by using particles swarm optimization and tabu search algorithms we optimized two-sided risk measures . A standard exact penalty function transforms the considered portfolio selection problem into an equivalent unconstrained minimization problem. And in final...
متن کاملNovel Radial Basis Function Neural Networks based on Probabilistic Evolutionary and Gaussian Mixture Model for Satellites Optimum Selection
In this study, two novel learning algorithms have been applied on Radial Basis Function Neural Network (RBFNN) to approximate the functions with high non-linear order. The Probabilistic Evolutionary (PE) and Gaussian Mixture Model (GMM) techniques are proposed to significantly minimize the error functions. The main idea is concerning the various strategies to optimize the procedure of Gradient ...
متن کاملAppraisal of the evolutionary-based methodologies in generation of artificial earthquake time histories
Through the last three decades different seismological and engineering approaches for the generation of artificial earthquakes have been proposed. Selection of an appropriate method for the generation of applicable artificial earthquake accelerograms (AEAs) has been a challenging subject in the time history analysis of the structures in the case of the absence of sufficient recorded accelerogra...
متن کاملSECURING INTERPRETABILITY OF FUZZY MODELS FOR MODELING NONLINEAR MIMO SYSTEMS USING A HYBRID OF EVOLUTIONARY ALGORITHMS
In this study, a Multi-Objective Genetic Algorithm (MOGA) is utilized to extract interpretable and compact fuzzy rule bases for modeling nonlinear Multi-input Multi-output (MIMO) systems. In the process of non- linear system identi cation, structure selection, parameter estimation, model performance and model validation are important objectives. Furthermore, se- curing low-level and high-level ...
متن کامل