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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

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.

Graines de Troc 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

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...

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. C++ compilers are also available for a large choice of plateforms (with a special distinction for GCC, which is free software). The main idea beside the design of the framework is specified as one of the following keywords: Favorites Here is my list : Other These frameworks are not those which I would recommend, but they have some properties that could be intersting :

Permaculture Qui en France connaît la permaculture ? Science, mode de pensée ou philosophie, la permaculture a été conceptualisée il y a près de 40 ans en Australie. Elle s’est popularisée dans les pays anglo-saxons (Australie, états-Unis, Angleterre [1]), mais a peiné à se faire connaître dans les pays francophones. Jusqu’à maintenant. DÉFINITION : La permaculture est une science de conception de cultures, de lieux de vie, et de systèmes agricoles humains utilisant des principes d’écologie et le savoir des sociétés traditionnelles pour reproduire la diversité, la stabilité et la résilience des écosystèmes naturels [3] Le but de la permaculture est de créer, par une conception (design) réfléchie et efficace, des sociétés humaines respectueuses de la Nature et des Hommes. L’éthique de la permaculture Pour apporter un cadre de réflexion et d’action en accord avec son but, la permaculture se base sur une éthique, décomposée en trois grands principes [6] : Se soucier de la Terre Se soucier de l’Homme Notes

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"). Recently is increasing a (rather small, but interesting) controversy about the authorship of the Ant Colony Optimization (ACO) idea. The "orthodox" seminal paper about ACO is a technical report, written by Dorigo, Maniezzo and Colorni, submitted in 1991. As technical report are not a rigourous publication, perhaps a more pertinent citation would be the corresponding paper published in the proceedings of the First European Conference on Artificial Life, in 1992, or the Dorigo's PhD thesis, completed in 1991. Nowadays, M. Where is the controversy ? In my opinion, M.

Le réchauffement climatique Charte du site UOH Charte de modération Nous accueillons vos commentaires sur le site de l'UOH. La possibilité de commenter en ligne les ressources référencées sur le site de l'UOH est ouverte à tout le monde, chacun est libre d'y intervenir, de manière anonyme ou identifiée. Le but des commentaires est de permettre aux utilisateurs d'instaurer des échanges enrichissants à partir des ressources publiées sur ce site. Nous avons choisi de les modérer a posteriori pour les rendre plus vivants. Nos conseils d'utilisation et principes de modération : Nos fils de commentaires sont des espaces de discussions et d'échange. Avant de poster un commentaire, assurez-vous qu'il corresponde bien au sujet de discussion. Les utilisateurs des ressources qui pourraient être interpellés, surpris ou choqués par certains commentaires peuvent directement envoyer un mail à l'adresse suivante : webmaster@uoh.fr Histoire

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. For example, periodic functions such as sine produce segmented patterns with repetitions, while symmetric functions such as Gaussian produce symmetric patterns. Linear functions can be employed to produce linear or fractal-like patterns. Furthermore, unlike typical ANNs, CPPNs are applied across the entire space of possible inputs so that they can represent a complete image. CPPNs can be evolved through neuroevolution techniques such as NeuroEvolution of Augmenting Topologies (called CPPN-NEAT). Bibliography[edit] See also[edit] External links[edit]

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