Soran University Artificial Intelligence Module Specification 1
... practice to the main areas of classical AI as well as newer engineering approaches such as neural networks and genetic algorithms. Specialist areas such as experts systems, natural language processing are also explored. Practical sessions will involve programming using relevant languages and using e ...
... practice to the main areas of classical AI as well as newer engineering approaches such as neural networks and genetic algorithms. Specialist areas such as experts systems, natural language processing are also explored. Practical sessions will involve programming using relevant languages and using e ...
MATHEMATICS OF COMPUTATION Volume 72, Number 241, Pages 131–157 S 0025-5718(01)01371-0
... exact or approximate solutions due to the low regularity of the source term. Also they are known to be limited to one space dimension, and the scheme and theorem extend obviously to higher dimensions on rectangular grids. In order to avoid the need of BV bounds, we design a new method of investigati ...
... exact or approximate solutions due to the low regularity of the source term. Also they are known to be limited to one space dimension, and the scheme and theorem extend obviously to higher dimensions on rectangular grids. In order to avoid the need of BV bounds, we design a new method of investigati ...
Genetic Team Composition and Level of Selection in the Evolution
... different parts of their genome, for example when each agent’s behavior is controlled by a different section of a single team genome [11], [22], [28], [33]. In this case, agents can specialize on different functions, yet be genetically identical, just like specialized cells in a biological organism. ...
... different parts of their genome, for example when each agent’s behavior is controlled by a different section of a single team genome [11], [22], [28], [33]. In this case, agents can specialize on different functions, yet be genetically identical, just like specialized cells in a biological organism. ...
Unsupervised Feature Selection for the k
... Given an n × d matrix A, let Uk ∈ Rn×k (resp. Vk ∈ Rd×k ) be the matrix of the top k left (resp. right) singular vectors of A, and let Σk ∈ Rk×k be a diagonal matrix containing the top k singular values of A. If we let ρ be the rank of A, then Aρ−k is equal to A − Ak , with Ak = Uk Σk VkT . ∥A∥F and ...
... Given an n × d matrix A, let Uk ∈ Rn×k (resp. Vk ∈ Rd×k ) be the matrix of the top k left (resp. right) singular vectors of A, and let Σk ∈ Rk×k be a diagonal matrix containing the top k singular values of A. If we let ρ be the rank of A, then Aρ−k is equal to A − Ak , with Ak = Uk Σk VkT . ∥A∥F and ...
Lecture Slides (PowerPoint)
... Random-restart Hill-Climbing • Series of HC searches from randomly generated initial states until goal is found • Trivially complete • E[# restarts]=1/p where p is probability of a successful HC given a random initial state • For 8-queens instances with no sideways moves, p≈0.14, so it takes ≈7 ite ...
... Random-restart Hill-Climbing • Series of HC searches from randomly generated initial states until goal is found • Trivially complete • E[# restarts]=1/p where p is probability of a successful HC given a random initial state • For 8-queens instances with no sideways moves, p≈0.14, so it takes ≈7 ite ...
Pareto-Based Multiobjective Machine Learning: An
... than one objective, which naturally fall into the category of scalarized multiobjective learning. Similar to supervised learning, multiple objectives can be considered in data clustering as well. On the one hand, it is well recognized that the objective function defined in (2) is strongly biased tow ...
... than one objective, which naturally fall into the category of scalarized multiobjective learning. Similar to supervised learning, multiple objectives can be considered in data clustering as well. On the one hand, it is well recognized that the objective function defined in (2) is strongly biased tow ...
Full Dynamic Substitutability by SAT Encoding
... However, computing fully interchangeable values is believed to be intractable [8, 13, 15, 34] so local forms such as neighbourhood interchangeability are much more commonly used: Definition. A value a for variable v is neighbourhood interchangeable with value b if and only if for every constraint on ...
... However, computing fully interchangeable values is believed to be intractable [8, 13, 15, 34] so local forms such as neighbourhood interchangeability are much more commonly used: Definition. A value a for variable v is neighbourhood interchangeable with value b if and only if for every constraint on ...
Genetic algorithm
In the field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.