A Random Function Based Framework for Evolutionary Algorithms
نویسندگان
چکیده
Evolutionary algorithms EAs are stochas tic population based algorithms inspired by the natural processes of recombination mu tation and selection EAs are often em ployed as optimum seeking techniques A for mal framework for EAs is proposed in which evolutionary operators are viewed as map pings from parameter spaces to spaces of ran dom functions Formal de nitions within this framework capture the distinguishing charac teristics of the classes of recombination mu tation and selection operators A speci c EA the generalized fast messy genetic algo rithm is de ned within the proposed frame work
منابع مشابه
Gradient-based Ant Colony Optimization for Continuous Spaces
A novel version of Ant Colony Optimization (ACO) algorithms for solving continuous space problems is presented in this paper. The basic structure and concepts of the originally reported ACO are preserved and adaptation of the algorithm to the case of continuous space is implemented within the general framework. The stigmergic communication is simulated through considering certain direction vect...
متن کاملGradient-based Ant Colony Optimization for Continuous Spaces
A novel version of Ant Colony Optimization (ACO) algorithms for solving continuous space problems is presented in this paper. The basic structure and concepts of the originally reported ACO are preserved and adaptation of the algorithm to the case of continuous space is implemented within the general framework. The stigmergic communication is simulated through considering certain direction vect...
متن کاملAdaptive Rule-Base Influence Function Mechanism for Cultural Algorithm
This study proposes a modified version of cultural algorithms (CAs) which benefits from rule-based system for influence function. This rule-based system selects and applies the suitable knowledge source according to the distribution of the solutions. This is important to use appropriate influence function to apply to a specific individual, regarding to its role in the search process. This rule ...
متن کاملTechno-economic operation optimization of a HRSG in combined cycle power plants based on evolutionary algorithms: A case study of Yazd, Iran
In this research study, energy, exergy and economic analyses is performed for a combined cycle power plant (CCPP) with a supplementary firing system. The purpose of this analyses is to evaluate the economic feasibility of a CCPP by applying an optimization techniques based on Evolutionary algorithms. Actually, the evolutionary algorithms of Firefly, PSO and NSGA-II are applied to minimize the c...
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 کاملThe ensemble clustering with maximize diversity using evolutionary optimization algorithms
Data clustering is one of the main steps in data mining, which is responsible for exploring hidden patterns in non-tagged data. Due to the complexity of the problem and the weakness of the basic clustering methods, most studies today are guided by clustering ensemble methods. Diversity in primary results is one of the most important factors that can affect the quality of the final results. Also...
متن کامل