Search Results - (( evolution classification problem algorithm ) OR ( using selection path algorithm ))
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1
Differential evolution for neural networks learning enhancement
Published 2008“…Evolutionary computation is the name given to a collection of algorithms based on the evolution of a population toward a solution of a certain problem. …”
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2
A New Quadratic Binary Harris Hawk Optimization For Feature Selection
Published 2019“…In this study, twenty-two datasets collected from the UCI machine learning repository are used to validate the performance of proposed algorithms. A comparative study is conducted to compare the effectiveness of QBHHO with other feature selection algorithms such as binary differential evolution (BDE), genetic algorithm (GA), binary multi-verse optimizer (BMVO), binary flower pollination algorithm (BFPA), and binary salp swarm algorithm (BSSA). …”
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Automated path testing using the negative selection algorithm
Published 2017“…Negative selection algorithm (NSA) has been used in this paper to generate test data for path testing automatically. …”
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4
Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy
Published 2018“…A trend that has emerged recently is to make the algorithm parameters automatically adapt to different problems during optimization, thereby liberating the user from the tedious and time-consuming task of manual setting. …”
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5
Feature selection optimization using hybrid relief-f with self-adaptive differential evolution
Published 2017“…Hence, feature selection is embedded to select the most meaningful features based on their rank. Differential evolution (DE) is one of the evolutionary algorithms that are widely used in various classification domains. …”
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Artificial fish swarm optimization for multilayer network learning in classification problems
Published 2012“…Artificial Fish Swarm Algorithm (AFSA) as one of the NIC methods is widely used for optimizing the global searching of ANN.In this study, we applied the AFSA method to improve the Multilayer Perceptron (MLP) learning for promising accuracy in various classification problems.The parameters of AFSA: AFSA prey, AFSA swarm and AFSA follow are implemented on the MLP network for improving the accuracy of various classification datasets from UCI machine learning. …”
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Artificial Fish Swarm Optmization for Multilayernetwork Learning in Classification Problems
Published 2012“…In this study, we applied the AFSA method to improve the Multilayer Perceptron (MLP) learning for promising accuracy in various classification problems. The parameters of AFSA: AFSA prey, AFSA swarm and AFSA follow are implemented on the MLP network for improving the accuracy of various classification datasets from UCI machine learning. …”
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Optimisation of Environmental Risk Assessment Architecture using Artificial Intelligence Techniques
Published 2024“…By contrast, the results of the safe path selection model were compared with the results obtained using Dijkstra's algorithm and the Floyd-Warshall algorithm. …”
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Path planning for unmanned aerial vehicles using visibility line-based methods
Published 2011“…The proposed 2D path planning algorithms, on the contrary, select a relatively smaller number of vertices using the so-called base line (BL), thus they are computationally efficient. …”
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Optimal QoS aware multiple paths web service composition using heuristic algorithms and data mining techniques
Published 2014“…Second, both the constructive procedure (CP) and the complementary procedure (CCP) heuristic algorithms computed the optimization considering only the execution path that has been predicted by the runtime path prediction method for criteria selection, eight QoS characteristics are suggested after investigating related works on the area of web service and web service composition. …”
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11
A novel Bezier curve control point search algorithm for autonomous navigation using N-order polynomial search with boundary conditions
Published 2021“…However, the Bezier curve is the most widely used in the curve fitting algorithm for vehicle path planning as it can generate a possible number of path maneuvers. …”
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Design Of Feature Selection Methods For Hand Movement Classification Based On Electromyography Signals
Published 2020“…Therefore, this thesis aims to solve the feature selection problem in EMG signals classification and improve the classification performance of EMG pattern recognition system. …”
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Route optimization using shortest path method / Muhamad Faisal Amin Shakri
Published 2025“…This study investigated the implementation and comparison between well known shortest path method which are Dijkstra’s algorithm, Bellman-Ford, and A* algorithm. …”
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14
A Continuous Overlay Path Probing Algorithm For Overlay Networks
Published 2013“…Such an in-band probing algorithm reduces measurement overhead on the selected overlay path. …”
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15
Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh
Published 2015“…Differential evolution (DE) is a simple yet powerful evolutionary algorithm (EA). …”
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16
Path planning for visually impaired people in an unfamiliar environment using particle swarm optimization
Published 2015“…Here, in this paper, we propose a path planning with predetermined waypoints method using Particle Swarm Optimization (PSO) algorithm. …”
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A hybrid sampling-based path planning algorithm for mobile robot navigation in unknown environments
Published 2013“…These algorithms generate paths using random numbers and perform efficiently in guiding the robot in known environments. …”
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18
Shortest Path Routing Using Heuristic Search
Published 2006“…In this project A* algorithm was used, to suggest shortest path model between two selected points to find the fastest and shortest route on Malaysia map. …”
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19
Artificial neural network learning enhancement using Artificial Fish Swarm Algorithm
Published 2011“…Artificial Neural Network (ANN) is a new information processing system with large quantity of highly interconnected neurons or elements processing parallel to solve problems.Recently, evolutionary computation technique, Artificial Fish Swarm Algorithm (AFSA) is chosen to optimize global searching of ANN.In optimization process, each Artificial Fish (AF) represents a neural network with output of fitness value.The AFSA is used in this study to analyze its effectiveness in enhancing Multilayer Perceptron (MLP) learning compared to Particle Swarm Optimization (PSO) and Differential Evolution (DE) for classification problems.The comparative results indeed demonstrate that AFSA show its efficient, effective and stability in MLP learning.…”
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Improving Ant Swarm Optimization With Embedded Vaccination For Optimum Reducts Generation
Published 2011“…Unlike a conventional PSOIACO algorithm, this hybrid algorithm shows improvement of the classification accuracy in its generated rough reducts to solve NP-Hard problem. …”
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