Search Results - learning a differences ((evolution algorithm) OR (evolutionary algorithm))*
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Developing a hybrid model for accurate short-term water demand prediction under extreme weather conditions: a case study in Melbourne, Australia
Published 2024“…Hybrid model development included the optimization of ANN coefficients (its weights and biases) using adaptive guided differential evolution algorithm. Post-optimization ANN model was trained using eleven different leaning algorithms. …”
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Broadening selection competitive constraint handling algorithm for faster convergence
Published 2020“…In this study, the BSCCH algorithm has been coupled with Differential Evolution algorithm as a proof of concept because it is found to be an efficient algorithm in the literature for constrained optimization problems. …”
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Development of a Bioinspired optimization algorithm for the automatic generation of multiple distinct behaviors in simulated mobile robots
Published 2006“…This research explores a new approach of using a multi-objective evolutionary algorithm (MOEA) to evolve robot controllers in performing phototaxis tasks while avoiding obstacles in a simulated 30 physics environment, to overcome problems involving more than one objective, where these objectives usually trade-off among each other and are expressed in different units. …”
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Automating commercial video game development using computational intelligence
Published 2011“…Our approach is to use a simple but powerful evolutionary algorithm called Evolution Strategies (ES) to evolve the connection weights and biases of feed-forward Artificial Neural Networks (ANN) and to examine its learning ability through computational experiments in a non-deterministic and dynamic environment, which is the well-known arcade game, called Ms. …”
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Towards Software Product Lines Optimization Using Evolutionary Algorithms
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Different mutation and crossover set of genetic programming in an automated machine learning
Published 2020“…As a family of evolutionary based algorithm, the effectiveness of Genetic Programming in providing the best machine learning pipelines for a given problem or dataset is substantially depending on the algorithm parameterizations including the mutation and crossover rates. …”
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Different mutation and crossover set of genetic programming in an automated machine learning
Published 2020“…As a family of evolutionary based algorithm, the effectiveness of Genetic Programming in providing the best machine learning pipelines for a given problem or dataset is substantially depending on the algorithm parameterizations including the mutation and crossover rates. …”
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The Evolutionary Convergent Algorithm: A Guiding Path of Neural Network Advancement
Published 2025“…Therefore, proposing an efficient meta-heuristic to improve the inputs of the trainer in ML would be significant. In this study, a new idea centered on seed growth, Seed Growth Algorithm (SGA), as a conditional convergent evolutionary algorithm is proposed for optimizing several discrete and continuous optimization problems. …”
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Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects
Published 2014“…In addition, one of the proposed EANNs incorporates a facility to learn overlapping samples of different classes from the imbalanced data environment. …”
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Optimized processing of satellite signal via evolutionary search algorithm
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Neural Controller Utilizing Genetic Algorithm Technique For Dynamic Systems
Published 2009“…This research presents a method of learning multilayer Neural Network (NN) using Genetic Algorithms (GAs) techniques. …”
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Dual optimization approach in discrete Hopfield neural network
Published 2024“…To evaluate the effectiveness of the Hybrid Differential Evolution Algorithm and Swarm Mutation in the learning and retrieval phases, several performance metrics are employed in terms of synaptic weight management, learning errors, testing errors, energy profiles, solution variations, and similarity for 10 different cases. …”
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Optimization Of Two-Dimensional Dual Beam Scanning System Using Genetic Algorithms
Published 2008“…Also, this research involves in developing a machine-learning system and program via genetic algorithm that is capable of performing independent learning capability and optimization for scanning sequence using novel GA operators. …”
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Implementation of evolutionary computing models for reference evapotranspiration modeling: Short review, assessment and possible future research directions
Published 2019“…Among several machine learning models, evolutionary computing (EC) has demonstrated a remarkable progression in the modeling of ETo. …”
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An efficient anomaly intrusion detection method with evolutionary neural network
Published 2020“…The third proposed method is a new Evolutionary Neural Network (ENN) algorithm with a combination of Genetic Algorithm and Multiverse Optimizer (GAMVO) as a training part of ANN to create efficient anomaly-based detection with low false alarm rate. …”
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An improved negative selection algorithm based on the hybridization of cuckoo search and differential evolution for anomaly detection
Published 2018“…The V-Detectors algorithm depends greatly on the random detectors generated in monitoring the status of a system. …”
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