Catégorie:Arbre (structure de données) Algorithm. Binary search algorithm. In computer science, a binary search or half-interval search algorithm finds the position of a specified input value (the search "key") within an array sorted by key value. For binary search, the array should be arranged in ascending or descending order.
In each step, the algorithm compares the search key value with the key value of the middle element of the array. If the keys match, then a matching element has been found and its index, or position, is returned. Otherwise, if the search key is less than the middle element's key, then the algorithm repeats its action on the sub-array to the left of the middle element or, if the search key is greater, on the sub-array to the right.
Liste des algorithmes. Métaheuristique. Catégorie:Algorithmique. Recherche tabou. Metaheuristics_doe_en.png (Image PNG, 1024x768 pixels) Catégorie:Algorithmique. Recherche tabou. Metaheuristics_doe_en.png (Image PNG, 1024x768 pixels) Fichier:Metaheuristics classification fr.svg - Wikipédia. Metaheuristics Network. Théorie de la complexité des algorithmes. Evolutionary algorithm. Evolutionary algorithms often perform well approximating solutions to all types of problems because they ideally do not make any assumption about the underlying fitness landscape; this generality is shown by successes in fields as diverse as engineering, art, biology, economics, marketing, genetics, operations research, robotics, social sciences, physics, politics and chemistry.
In most real applications of EAs, computational complexity is a prohibiting factor. In fact, this computational complexity is due to fitness function evaluation. Fitness approximation is one of the solutions to overcome this difficulty. However, seemingly simple EA can solve often complex problems; therefore, there may be no direct link between algorithm complexity and problem complexity.
A possible limitation [according to whom?] Evolutionary art. Artificial Evolution of the Cyprus Problem (2005) is an artwork created by Genco Gulan Evolutionary art is created using a computer.
The process starts by having a population of many randomly generated individual representations of artworks. Each representation is evaluated for its aesthetic value and given a fitness score. Compositional pattern-producing network. Compositional pattern-producing networks (CPPNs), are a variation of artificial neural networks (ANNs) which differ in their set of activation functions and how they are applied.
While ANNs often contain only sigmoid functions (and sometimes Gaussian functions), CPPNs can include both types of functions and many others. The choice of functions for the canonical set can be biased toward specific types of patterns and regularities. Novelty Search Users Page. "To achieve your highest goals, you must be willing to abandon them.
" 2013 Keynote Now in High Quality on Youtube: Ken Stanley gives a keynote at the 16th Portuguese Conference on Artificial Intelligence: " When Algorithms Inform Real Life: Novelty Search and the Myth of the Objective" 2012 YouTube Video: Ken Stanley gives Joint ACM and NICTA-sponsored 2012 talk at RMIT on "Discovery Without Objectives" HyperNEAT. Hypercube-based NEAT, or HyperNEAT, is a generative encoding that evolves artificial neural networks (ANNs) with the principles of the widely used NeuroEvolution of Augmented Topologies (NEAT) algorithm. It is a novel technique for evolving large-scale neural networks utilizing the geometric regularities of the task domain.
It uses Compositional Pattern Producing Networks  (CPPNs), which are used to generate the images for Picbreeder.org and shapes for EndlessForms.com. HyperNEAT has recently been extended to also evolve plastic ANNs  and to evolve the location of every neuron in the network. Applications to Date Neuroevolution of augmenting topologies. Dreaming of Metaheuristics. One of the scientist key policy is always to refer to people who did the first work (as it is pointed out by the "hard blogging scientists" manifest).
It is due to the fact that researchers want to share free science with everybody (at least at little cost), and that recognition is a form of remuneration (in a similar way, Eric S. Raymond explain such mechanism for hackers, in his essay "The Cathedral and the Bazaar"). Touchgraph_books.png (Image PNG, 1077x909 pixels) Dreaming of Metaheuristics. Note that descriptions are picked up from the web sites of the projects.
As one can see, most of these softwares are designed for evolutionnary algorithms, but I recommend you to try out some of the generic frameworks, because "genetic" algorithms are not always the best choice for solving an optimization problem, despite their wide spread. Here are the frameworks I would recommend. These frameworks are free softwares, you can use, look at the code, modify it and redistribute it (precious qualities for frameworks).
I would also recommend C or C++, which permits to implement fast programs, while using object oriented programming. Revues.org : portail de revues en sciences humaines et sociales. Heuristic approach to the airline schedule disturbances problem - Transportation Planning and Technology. Taxonomy of Metaheuristics & Software Engineering. Algorithmic.net: algorithmic composition resources. Grid-theme-naga1e.jpg (Image JPEG, 800x600 pixels) Nicolas HERVE Main/Algo Tri. Portail:Algorithmique. Une page de Wikipédia, l'encyclopédie libre.
Portail Algorithmique On désigne par algorithmique l’ensemble des activités logiques qui relèvent des algorithmes ; en particulier, en informatique, cette discipline désigne l'ensemble des règles et des techniques qui sont impliquées dans la définition et la conception des algorithmes. Le mot vient du nom du mathématicien Al-Khuwarizmi (latinisé au Moyen Âge en Algoritmi), qui, au IXe siècle écrivit le premier ouvrage systématique sur la solution des équations linéaires et quadratiques.