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Neuroevolution of augmenting topologies

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] Related:  OPERATIONAL RESEARCH

Meta-Optimizing Semantic Evolutionary Search Meta-optimizing semantic evolutionary search (MOSES) is a new approach to program evolution, based on representation-building and probabilistic modeling. MOSES has been successfully applied to solve hard problems in domains such as computational biology, sentiment evaluation, and agent control. Results tend to be more accurate, and require less objective function evaluations, than other program evolution systems, such as genetic programming or evolutionary programming . Best of all, the result of running MOSES is not a large nested structure or numerical vector, but a compact and comprehensible program written in a simple Lisp-like mini-language. A discussion of how MOSES fits into the grand scheme of OpenCog is given on the OpenCogPrime:Probabilistic Evolutionary Learning Overview page. Overview MOSES performs supervised learning, and thus requires either a scoring function or training data to be specified as input. More precisely, MOSES maintains a population of demes. Documentation Code

Network topology A good example is a local area network (LAN): Any given node in the LAN has one or more physical links to other devices in the network; graphically mapping these links results in a geometric shape that can be used to describe the physical topology of the network. Conversely, mapping the data flow between the components determines the logical topology of the network. Topology[edit] There are two basic categories of network topologies:[4] Physical topologiesLogical topologies The shape of the cabling layout used to link devices is called the physical topology of the network. The logical topology in contrast, is the way that the signals act on the network media, or the way that the data passes through the network from one device to the next without regard to the physical interconnection of the devices. Diagram of different network topologies. The study of network topology recognizes eight basic topologies:[5] Point-to-pointBusStarRing or circularMeshTreeHybridDaisy chain Point-to-point[edit]

HyperNEAT Hypercube-based NEAT, or HyperNEAT,[1] is a generative encoding that evolves artificial neural networks (ANNs) with the principles of the widely used NeuroEvolution of Augmented Topologies (NEAT) algorithm.[2] 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 [3] (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 [4] and to evolve the location of every neuron in the network.[5] Applications to Date[edit] Multi-agent learning [6]Checkers board evaluation [7]Controlling Legged Robots [8][9][10][11][12][13]videoComparing Generative vs. Direct Encodings [14][15][16]Investigating the Evolution of Modular Neural Networks [17][18][19]Evolving Objects that can be 3D Printed [20]Evolving the Neural Geometry and Plasticity of an ANN [21] References[edit] Jump up ^ K.

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. A possible limitation [according to whom?] Implementation of biological processes[edit] Evolutionary algorithm types[edit] Similar techniques differ in the implementation details and the nature of the particular applied problem. Related techniques[edit] Swarm algorithms, including: [edit] See also[edit] References[edit] Bibliography[edit] External links[edit]

Graines de Troc NeuroEvolution of Augmenting Topologies I created this page because of growing interest in the use and implementation of the NEAT method. I have been corresponding with an expanding group of users. Because the same points come up more than once, it makes sense to have a place where people can come and tap into the expanding knowledge we have about the software and the method itself. We also developed an extension to NEAT called HyperNEAT that can evolve neural networks with millions of connections and exploit geometric regularities in the task domain. The HyperNEAT Page includes links to publications and a general explanation of the approach. New! Tutorial Available: Wesley Tansey has provided a helpful tutorial on setting up a Tic-Tac-Toe experiment in SharpNEAT 2. NEAT Software FAQ - Questions that mostly relate to coding issues or using the actual software. First, how closely does the package you want follow my (Ken's) original NEAT source code? Second, what is your favorite platform? Third, what language do you prefer?

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" 2010 Video: SPLASH 2010 Keynote on Searching Without Objectives YouTube Video: Bird Flying Behavior Evolved with Novelty Search by Ander Taylor. This page provides information on the use and implementation of novelty search, an evolutionary search method that takes the radical step of ignoring the objective of search and instead rewarding only behavioral novelty. Please direct inquiries to Ken Stanley, kstanley@eecs.ucf.edu (Website) or Joel Lehman, jlehman@eecs.ucf.edu (Website) Novelty search: Evolution without objectives More than an approach to solving problems Novelty Search Publications

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]

cultiver - cultiver -- les basiques -- objectifs alimentaires - cultiver... cultiver ce que l'on mange est une activité essentielle, qui en autarcie, occupe une bonne partie du temps... c'est surtout du printemps à l'automne qu'elle mobilise il y a différentes façons de voir et de faire... selon ce que l'on veut obtenir, selon le mode de vie et d'alimentation, selon l'endroit où l'on se trouve, aussi : - un pourcentage important de cueillettes sauvages va permettre d'avoir à moins à travailler la terre à la production agricole. mais si la plupart des plantes sauvages sont comestibles, (bon à savoir en cas de nécessité), c'est surtout une minorité d'entre elles qui sont intéressantes car elles poussent rapidement et à profusion... pour remplir la marmite et faire manger une famille, par exemple, il faut pas mal de quantités, régulièrement, et seules certaines plantes remplissent cette condition. il y a diverses façons aussi de concevoir la rentabilité : H.J. objectifs alimentaires...

RoboRoach : un cyborg mi robot, mi… cafard en vente ! Vous aimez nos articles ? Suivez nous sur facebook Vous aimez nos articles ? Suivez nous sur twitter Déjà 204 réaction(s),partagez cet article avec vos amis ! Il y a quelques mois un projet complètement dingue a germé sur le site Kickstarter. Ce projet à l’allure très amusant peut aussi faire froid dans le dos. Les cafards utilisent les antennes qu’ils ont sur la tête pour se déplacer, elles sont très sensibles aux odeurs et à ce qu’elles touchent. Le principe du RoboRoach est simple, c’est une fusion de neurosciences comportementales et d’ingénierie des neurones (oui j’ai dit simple). Pour recevoir cette puce, le cafard doit subir une intervention chirurgicale (pauvre petite bête), mais rassurez-vous, c’est sous anesthésie ! Et vous qu’en pensez-vous ? Voici le principe en vidéo :

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