Open Computing Language (OpenCLGenetic Algorithm (GA) is effective and robust method for solving many optimization problems. However, it may take more runs (iterations) and time to get optimal solution. The execution time to find the optimal solution also depends upon the niching-technique applied...
Karpouzis K, Yannakakis GN (2016) Emotion in games. Springer Book Google Scholar Kimbrough SO, Koehler GJ, Lu M, Wood DH (2008) On a feasible–infeasible two-population (fi-2pop) genetic algorithm for constrained optimization: Distance tracing and no free lunch. Eur J Oper Res 190(2)...
DISCERNA neural network package for natural language processing LENSNeural Network simulator NEURONfor computer simulations of neurons and neural networks and the accompanyingTUTORIAL NNUGANeural Network Using Genetic Algorithms PINNPseudo-Inverse associative Neural Networks, a library in C++ Radial Basis Func...
Firstly, soft computing which is the fusion or combination of fuzzy systems, neural networks and genetic algorithms is studied. Then, by taking advantages of fuzzy systems and neural networks a novel fuzzy-neural network with a general parameter learning algorithm and system structure determination is...
Classical frameworks such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Simulated Annealing (SA) are examples of such frameworks (Dokeroglu, Sevinc, Kucukyilmaz, & Cosar, 2019). Moreover, there is a large body of literature that addresses ...
Understanding the behavior behind the AI algorithm is a hard task, and even more so when trying to understand how it learns. In this field, Project Malmo stands out for enabling player and AI agent interaction, a feature not present in Godot and Unreal Engine. Unity, on the other hand, ...
Various soft computing methods [19–26] based on the artificial neural network and genetic algorithm are also the research hotspots in recent years. A comparative study between different soft computing-based methods (artificial neural network, adaptive neuro-fuzzy inference system, and genetic algorithm...
To address this issue, we present Cancer-Finder, a domain generalization-based deep-learning algorithm that can rapidly identify malignant cells in single-cell data with an average accuracy of 95.16%. More importantly, by replacing the single-cell training data with spatial transcriptomic datasets, ...
Hence, resorting to efficient approaches and high performance computing is required in order to reduce the execution time. A general purpose software that provides an integration between deterministic solvers (i.e. finite element solvers), efficient algorithms for uncertainty management and high ...
It is straightforward to extend decision trees to incorporate IPCW: individual cases in the training set are assigned weights ωi as described above, and the ωi are used as “case weights” in the decision tree algorithm. With IPCW, we calculate a weighted decrease in Gini impurity,ΔIGω(...