Objective-function algorithm for advance data
OPTIMIZATION OF AN OBJECTIVE FUNCTION BY USING GENETIC ALGORITHM
... the algorithm tries to climb up from the current solution to reach a ...the algorithm behaves just like a stochastic hill climbing ...the algorithm is almost certain to find the global ...
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A Fast Algorithm for Computing the Deceptive Degree of an Objective Function
... a function easier. In the final, we describes the fast algorithm and analyses the complexity of the ...fast algorithm is helpful to discussing the usefulness of the new definition of ...given ...
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A Penalty Function Algorithm with Objective Parameters and Constraint Penalty Parameter for Multi Objective Programming
... an algorithm to solve the inequality constrained multi-objective pro- gramming (MP) by using a penalty function with objective parameters and constraint penalty pa- ...penalty function ...
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Firefly algorithm for economic emission dispatch with normalized objective function
... fireflies, or lighting bugs, in the summer sky in the tropical temperature regions. It was developed by Dr. Xin-She Yang at Cambridge University in 2007, and it is based on the swarm behavior such as fish, insects, or ...
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Operator Objective Function Guidance for a Real-time Unmanned Vehicle Scheduling Algorithm
... path-planning algorithm, and thus it is likely that they were not aware of all of the variables and constraints that the algorithm took into account when creating ...the algorithm ’s objective ...
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An Evolutionary Multi-Objective Crowding Algorithm (EMOCA): Benchmark Test Function Results
... Genetic Algorithm II (NSGA-II) NSGA –II [2] assigns a Pareto rank to each individual based on a non-dominated sorting ...The algorithm combines attractive features such as elitism, fast non-dominated ...
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GeNePi: a Multi-Objective Machine Reassignment Algorithm for Data Centres
... every objective; Ne, based on a genetic algorithm called NSGA-II that mixes solutions of the initial population and tries to find new solutions (and more diverse ones); and Pi a local search that looks for ...
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Our algorithm is very robust, placing no requirements on the problem data such as positive definiteness of the objective function or linear independence of the constraint functions
... Our algorithm is very robust, placing no requirements on the problem data such as positive definiteness of the objective function or linear independence of the constraint ...
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Optimal Location and Capacity of Distributed Generation for Multi-objective Function using Bat Algorithm
... Bat Algorithm it is compared with standard particle swarm optimization ...Bat Algorithm and PSO are in close ...of algorithm and min VSI is the minimum value of VSI, among all multiple runs in that ...
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Assessing the performance of a differential evolution algorithm in structural damage detection by varying the objective function
... the objective function used. In this study, four objective functions were used, and the most reliable results were obtained by objective function F1, which is based on natural ...
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Imputing a Convex Objective Function
... an objective function is a prac- tical problem that arises frequently, and is closely related to problems in machine learning, dynamic programming, robotics, and ...convex objective function, ...
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A Multi Objective Genetic Algorithm for Program Partitioning and Data Distribution Using TVRG
... an algorithm that per- forms data distribution and parallelization ...taneous algorithm are to reduce the length of critical path and the total memory ...
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Cluster Interfaced Objective Function for Decision Tree Classifiers for Mining Data with Uncertainty
... goal function and ...intention function, the entire depend variety of entropy calculations consists of the quantity of calculated lower ...Interfaced objective function, it's miles recognized ...
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Multi-objective Algorithm for Optimal Design
... Multi-objective Algorithm for Optimal Design Abubaker Mohamed Elbayoudi Abstract- Multiobjective optimization is progressively more applied as it allows being closer to real engineering problems that may be ...
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Design Complexity for Objective Function Points
... in Data Functions and all referenced file types are counted as FTR in Transactions ...reference function point values for all function types namely the ILF, EIF, EI, EO and EQ, with respect to the ...
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Evolving temporal fuzzy itemsets from quantitative data with a multi objective evolutionary algorithm
... For example, consider a product item in a supermarket, it may be available for sale only during a particular seasonal period, such as British asparagus during summer. Its support since it was introduced is high but its ...
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Classifying biological Data based on Association rule using Multi Objective Genetic Algorithm
... 3. mutation which introduces random modifications. Multi-Objectives Genetic Algorithm[10] Being a population based approach, GA are well suited to solve multi-objective optimization problems. A generic ...
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Analytical Method of Multi Objective Genetic Algorithm with Multi Objective Messy Genetic Algorithm in Satellite Image Segmentation
... Genetic Algorithm (MO-GA) for the selection of spectral signature from satellite images is ...clustering algorithm to approximately4 times the speed of the random based selection of ...Genetic ...
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Text Data Fusion to Speech Signal Using Hash Function Algorithm
... text data when digital contents are commonly distributed by Internet and more security attacks are appeared from eavesdropping, data exposure and data ...A function called hash function ...
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A single-objective and a multi-objective genetic algorithm to generate accurate and interpretable fuzzy rule based classifiers for the analysis of complex financial data
... single objective algorithm for the generation of accurate rule bases The aim of this algorithm is to go beyond the limitations of the rule selection approach which assumes that a set of candidate ...
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