Evolutionary Algorithm
Embodied Evolution: Distributing an evolutionary algorithm in a population of robots
... the evolutionary process itself has no central lo- cus of control, and is distributed in a population of robots—not merely that each robot has a physical ...the evolutionary algorithm maintains and ...
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Hybrid adaptive evolutionary algorithm based on decomposition
... an evolutionary algorithm framework to find the most suitable search operator for the given ...line algorithm but also saves on the time that is necessary to find the operators that perform best in ...
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Safety Management in NPPs Using Evolutionary Algorithm
... The TS&M requirements for increasing the system availability and for minimizing the risk due to the probable postulated initiating events have brought a growing interest to focus on multi objective optimization in ...
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Evolutionary algorithm for intelligent hybrid system training
... the evolutionary algorithm of the adaptive hybrid control system ...This algorithm differs by the application of combined operators of random changes and the possibility of dynamic correction of ...
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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 ...indicator-based evolutionary ...
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An evolutionary algorithm for graph planarisation by vertex deletion
... heuristic algorithm that uses vertices deletion to modify a non-planar graph in order to obtain a planar ...proposed algorithm aims to delete a minimum number of vertices to achieve its ...approximation ...
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EVOLUTIONARY ALGORITHM: A CLASSICAL SEARCH AND OPTIMIZATION TECHNIQUE
... Evolutionary algorithm belongs to class of general stochastic search ...of Evolutionary computation which is a generic population based metaheuristic optimization algorithm ...search ...
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Evolutionary Algorithm for Scheduling in Wireless Sensor Networks
... The main goal of this paper is to optimize both the total time spent in transmission and the energy consumption by switching between the active and sleep states. Other goals were also considered such as: packet delay, ...
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A novel multiobjective evolutionary algorithm based on regression analysis
... of evolutionary algorithm and local heuristics search outperforms traditional evolutionary algorithms in a wide variety of scalar objective optimization problems [4, ...
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An Evolutionary Algorithm for Automatic Summarization
... an evolutionary algorithm rather than an ILP-based method, the score com- putation and constraints are totally free and not limited by ILP modelization ...
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Differential evolution with an evolution path : a DEEP evolutionary algorithm
... an evolutionary process, this paper proposes a predic- tive approach to the reproduction mechanism of new individuals for differential evolution (DE) ...new evolutionary algorithm (EA) framework, ...
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Implementation of Clustering For Data Aggregation Using Evolutionary Algorithm
... Genetic algorithm is a class of developmental ...using evolutionary algorithm in ...using evolutionary algorithm such as Genetic Algorithm ...
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Better Implementation of Evolutionary Algorithm through Mutant Function
... Mutation and recombination and the fitness setting trust on our choice of representation scheme for Evolutionary Algorithms. The illustration should consequently make mutation and recombination behave like the ...
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Hyperspectral classification optimization using multiobjective evolutionary algorithm
... Multiobjective Evolutionary Algorithm (MOEA), which is based on the genetic algorithm, to find the optimal threshold value of each of the five spectral similarity measures in identifying different ...
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Tchebycheff Method-based Evolutionary Algorithm for Multiobjective Optimization
... Genetic Algorithm (NSGA- II) [12], Strength Pareto Evolutionary Algorithm (SPEA-II) [27], Multiobjective Genetic Algorithm (MOGA) [15], Niched Pareto Genetic Algorithm (NPGA) ...
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Evolutionary Algorithm A Stochastic Approach For Optimal Load Delivery
... It is therefore of great importance to solve this problem as quickly and accurately as possible. The conventional methods such as Lambda Iteration method, Newton’s method, Base Point and Participation factor method etc ...
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A Weight Coded Evolutionary Algorithm for the Multidimensional Knapsack Problem
... An Evolutionary algorithm (EA) is a generic population-based metaheuristic opti- mization algorithm. Candidate solutions to the optimization problem play the role of individuals (parents) in a ...
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Fuzzy Preference Incorporated Evolutionary Algorithm for Multiobjective Optimization
... In this study, the incorporation of fuzzy preference of the decision maker has been discussed and simulation on test functions has been done. From the simulation results it is clear to see that by incorporating the fuzzy ...
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A dynamic multiobjective evolutionary algorithm based on a dynamic evolutionary environment model
... the algorithm to the new optimal ...co-evolutionary algorithm; it generates new individuals by selecting representatives of different sub-populations and themselves to recombine and ...
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Minimization of Real Power loss using Evolutionary Algorithm
... regard, evolutionary algorithm have been used that can give better solution in several conditions and can easily applied in field of science and number of branches engineering ...
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