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Evolutionary algorithm

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[citation needed]. 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?] Implementation of biological processes[edit] Evolutionary algorithm types[edit] Related techniques[edit] Swarm algorithms, including: [edit] See also[edit] References[edit] Related:  OPERATIONAL RESEARCHFuture Collaborations with Mrinal

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. Evolutionary art is a branch of Generative art, which system is characterized by the use of evolutionary principles and natural selection as generative procedure. In common with natural selection and animal husbandry, the members of a population undergoing artificial evolution modify their form or behavior over many reproductive generations in response to a selective regime. In interactive evolution the selective regime may be applied by the viewer explicitly by selecting individuals which are aesthetically pleasing. See also[edit] Further reading[edit] Conferences[edit] "Evomusart. 1st International Conference and 10th European Event on Evolutionary and Biologically Inspired Music, Sound, Art and Design" External links[edit]

Interactive evolutionary computation Interactive evolutionary computation (IEC) or aesthetic selection is a general term for methods of evolutionary computation that use human evaluation. Usually human evaluation is necessary when the form of fitness function is not known (for example, visual appeal or attractiveness; as in Dawkins, 1986[1]) or the result of optimization should fit a particular user preference (for example, taste of coffee or color set of the user interface). IEC design issues[edit] The number of evaluations that IEC can receive from one human user is limited by user fatigue which was reported by many researchers as a major problem. However IEC implementations that can concurrently accept evaluations from many users overcome the limitations described above. IEC types[edit] IEC methods include interactive evolution strategy,[4] interactive genetic algorithm,[5][6] interactive genetic programming,[7][8][9] and human-based genetic algorithm.,[10] IGA[edit] See also[edit] References[edit] External links[edit]

Neuroevolution of augmenting topologies NeuroEvolution of Augmenting Topologies (NEAT) is a genetic algorithm for the generation of evolving artificial neural networks (a neuroevolution technique) developed by Ken Stanley in 2002 while at The University of Texas at Austin. It alters both the weighting parameters and structures of networks, attempting to find a balance between the fitness of evolved solutions and their diversity. It is based on applying three key techniques: tracking genes with history markers to allow crossover among topologies, applying speciation (the evolution of species) to preserve innovations, and developing topologies incrementally from simple initial structures ("complexifying"). Performance[edit] On simple control tasks, the NEAT algorithm often arrives at effective networks more quickly than other contemporary neuro-evolutionary techniques and reinforcement learning methods.[1][2] Complexification[edit] Implementation[edit] Extensions to NEAT[edit] rtNEAT[edit] Phased Pruning[edit] HyperNEAT[edit]

Théorie de la complexité des algorithmes Un article de Wikipédia, l'encyclopédie libre. La théorie de la complexité est un domaine des mathématiques, et plus précisément de l'informatique théorique, qui étudie formellement la quantité de ressources (en temps et en espace) nécessaire pour la résolution de problèmes au moyen de l'exécution d'un algorithme. Il s'agit donc d'étudier la difficulté intrinsèque de problèmes posés mathématiquement. Un algorithme répond à un problème. Il est composé d'un ensemble d'étapes simples nécessaires à la résolution, dont le nombre varie en fonction du nombre d'éléments à traiter. D'autre part, plusieurs algorithmes peuvent répondre à un même problème. La théorie de la complexité s'attache à connaître la difficulté (ou la complexité) d'une réponse par algorithme à un problème, dit algorithmique, posé de façon mathématique. La théorie de la complexité étudie principalement (mais pas uniquement) les problèmes de décisions. Un exemple de problème de décision est: TIME(t(n)) NTIME(t(n)) SPACE(s(n))

Tierra (computer simulation) Tierra is an abstract model, but any quantitative model is still subject to the same validation and verification techniques applied to more traditional mathematical models, and as such, has no special status. The creation of more detailed models in which more realistic dynamics of biological systems and organisms are incorporated is now an active research field (see systems biology). Jump up ^ Ray, Thomas. "What this Program is". Retrieved 3 January 2014. Jump up ^ Ray, Thomas. Bentley, Peter, J. 2001, "Digital Biology:How Nature is transforming Our Technology and Our Lives", Simon & Schuster, New York, NY. Tierra home page

Procedural generation Procedural generation is a widely used term in the production of media; it refers to content generated algorithmically rather than manually. Often, this means creating content on the fly rather than prior to distribution. This is often related to computer graphics applications and video game level design. Overview[edit] The term procedural refers to the process that computes a particular function. The modern demoscene uses procedural generation to package a great deal of audiovisual content into relatively small programs. In recent years, there has been an increasing interest in procedural content generation within the academic game research community, especially among researchers interested in applying artificial intelligence methods to the problems of PCG. Contemporary application[edit] Video games[edit] RoboBlitz used procedurally generated textures in order to reduce the file size of the game Furthermore, the number of unique objects displayed in a video game is increasing. Film[edit]

Metaheuristics Network Scientists create 'artificial evolution' for the first time Scientists have made a significant step towards developing fully artificial life – for the first time, they demonstrated evolution in a simple chemistry set without DNA. In a way, the researchers showed that the principle of natural selection doesn’t only apply to the biological world. Using a simple a robotic ‘aid’, a team from the University of Glasgow managed to create an evolving chemical system. They used an open source robot based upon a cheap 3D printer to create and monitor droplets of oil. Photographs of the droplet behaviour as a function of time (from left to right) for all the traits (given in a–i). The robot used a simple video camera to monitor, process and analyse the behaviour of 225 differently-composed droplets, identifying a number of distinct characteristics such as vibration or clustering. “This is the first time that an evolvable chemical system has existed outside of biology.

Game mechanics Game mechanics are constructs of rules intended to produce a game or gameplay. All games use mechanics; however, theories and styles differ as to their ultimate importance to the game. In general, the process and study of game design, or ludology, are efforts to come up with game mechanics that allow for people playing a game to have an engaging, but not necessarily fun, experience. Game mechanics vs. gameplay[edit] Gameplay refers to the overall game experience or essence of the game itself. For example, the basic gameplay of a shooting or fighting game is to hit while not being hit. However, from a programming or overall design perspective, basic gameplay can be deconstructed further to reveal constituent game mechanics. Game mechanics vs. theme[edit] Games that are mechanically similar can vary widely in theme. Some wargames, at the other extreme, are known for extremely complex rules and for attempts at detailed simulation. Turns[edit] Action points[edit] Auction or bidding[edit]

Recherche tabou Un article de Wikipédia, l'encyclopédie libre. La recherche tabou est une métaheuristique d'optimisation présentée par Fred Glover en 1986. On trouve souvent l'appellation recherche avec tabous en français. Cette méthode est une métaheuristique itérative qualifiée de recherche locale au sens large. Principe[modifier | modifier le code] L'idée de la recherche tabou consiste, à partir d'une position donnée, à en explorer le voisinage et à choisir la position dans ce voisinage qui minimise la fonction objectif. Il est essentiel de noter que cette opération peut conduire à augmenter la valeur de la fonction (dans un problème de minimisation) : c'est le cas lorsque tous les points du voisinage ont une valeur plus élevée. Le risque cependant est qu'à l'étape suivante, on retombe dans le minimum local auquel on vient d'échapper. Les positions déjà explorées sont conservées dans une file FIFO (appelée souvent liste tabou) d'une taille donnée, qui est un paramètre ajustable de l'heuristique.

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