Indicator Based Evolutionary Algorithms
A modified indicator based evolutionary algorithm (mIBEA)
... Abstract—Multi-objective evolutionary algorithms (MOEAs) based on the concept of Pareto-dominance have been successfully applied to many real-world optimisation ...towards ...
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Indicator-Based Evolutionary Level Set Approximation: Mixed Mutation Strategy and Extended Analysis
... of evolutionary level set approximation is to find a finite representation of a level set of a given black box ...create algorithms that quickly converge to feasible solutions and then achieve a good coverage ...
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CLASSIFICATIONS, ASSESSMENTS AND CHARACTERISTICS AS FACTORS TOWARDS ANALYZING ORGANIZATIONAL KNOWLEDGE
... solving algorithms can be adapted and applied including deferent variants and extensions of Multi-Objective Genetic Algorithms ...Pareto Evolutionary Algorithm - II (SPEA-II) and the ...
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GRID Scheduling algorithms: A survey
... scheduling algorithms such as Static, Dynamic, Ant Colony, Multi objective Evolutionary algorithms, Simulated Annealing, Taboo search and Genetic algorithms are discussed from different points ...
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Multi-objective optimisation using learning automata and its applications in power systems
... Nature has presented the significance of learning ability. For instance, in the wilderness which is full of dangers, each careless action could lead to death, thus creatures have to learn how to select the actions which ...
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An Intelligent Gear Fault Diagnosis Model Based on EMD and Evolutionary Algorithms
... learning algorithms like Artificial neural networks [6], ...reduction. Based on available literatures, it could be noted that the MSVM and ABC as a classifier has still an immense interest and very little ...
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On minimizing coding operations in network coding based multicast: an evolutionary algorithm
... efficient evolutionary algorithm to minimize the amount of coding operations required in network coding based ...multicast. Based on genetic algorithms, we adapt two extensions in the proposed ...
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Evolutionary processes in nature, technology and society –a few common trendsa
... which evolutionary approaches are applied to significantly different objects and processes, includes those branches which are especially interesting due to the implementation of the Darwin's concepts of variation, ...
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Evolutionary algorithms for scheduling operations
... Hanafi and Kozan (2014) combined SA with a constructive heuristic to solve a railway crew scheduling problem. The heuristic procedure firstly analyses the train routes and timetables. If the train starts at one depot, ...
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Clustering Analysis of Simple K – Means Algorithm for Various Data Sets in Function Optimization Problem (Fop) of Evolutionary Programming
... K-Means is one of the simplest unsupervised learning algorithms to classify a given data set through a certain number of clusters fixed a priori [14]. The term “k-means” was first used by James MacQueen in 1967, ...
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Evolutionary Jazz Improvisation - Jazz Harmony and Solo Improvisation Created by Means of Evolutionary Algorithms with Automatic Fitness
... using evolutionary algorithms to develop new artistic productions was first started on a broader scale in the digital graphics area, such as forerunner Karl Sims ...The evolutionary algorithms ...
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Self adaptation in evolutionary algorithms
... This issue has been tackled successfully within both Evolutionary Strategies and Evolutionary Programming by encoding the mutation step applied within the representation of each solution. This approach also ...
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Improvement of dynamic load balancing in Cloud Environment
... Author Madhurima Rana et al. in [6] discussed Load balancing that ensures no single node will be overloaded and used to distribute workload among multiple nodes improving the system performance and ensuring proper ...
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Network coding via evolutionary algorithms
... The joint optimisation of network parameters in the multicast scenario is a complex process and the evolutionary approach appears essential to overcome its complexity. Among the many network parameters, in which ...
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Investigating the parameter space of evolutionary algorithms
... was based on the self-adaptation of the first two parameters, which were encoded within an individual’s ...other evolutionary algorithms they ...
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Credit Assignment in Adaptive Evolutionary Algorithms
... fixed, finite population size, it is expected that most lineages perish to make room for a select few that flourish. In order to select operators that tend to produce those infrequent flourishing lineages, it is ...
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Evolutionary Algorithms to Aid Watershed Management
... EAGA is a multi-population approach where one subpopulation searches for the optimal solution to the original problem while the remaining subpopulations search for good, but maximally different alternatives [14]. The ...
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Genetic Algorithms and Programming-An Evolutionary Methodology
... is evolutionary art. In contrast to most GP applications, in evolutionary art, the user often acts directly as the fitness ...The evolutionary art community includes many artists and computing ...
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Proposing a Novel Cost Sensitive Imbalanced Classification Method based on Hybrid of New Fuzzy Cost Assigning Approaches, Fuzzy Clustering and Evolutionary Algorithms
... costs. One of the main problems in data mining is the class imbalance problem. It occurs, in the binary case, when there exists a considerable difference between the number of instances in each class. The minority class ...
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Solving The Active Distribution Network Reconfiguration (ADNR) Problem Taking Into Consideration A Stochastic Wind Scenario and Load Uncertainty By Using HBFDE Method
... is based on some simplifications in order to calculate the change of loss in load transfer from one feeder to another, was ...model based automated strategy was considered the DNR problem taking the smart ...
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