Evonet Wiki : Welcome to Evo* 2007 Gotcha! Criminal mugs captured in computer In Search of..... - TV.com www.tv.com/shows/in-search-of Narrarated by Leonard Nimoy, In search of was a 30 minute syndicated show that covered a wide range of paranormal topics. Search Engine - Download.com download.cnet.com/s/search-engine search engine free download - GSA Search Engine Ranker, Nomao - The personalized search engine, Zoom Search Engine, and many more programs Google Search - Download.com download.cnet.com/s/google-search google search free download - Google Search, Google Toolbar for Internet Explorer, Google Search, and many more programs Star Search - Episode Guide - TV.com www.tv.com/shows/star-search-2003/episodes Star Search episode guides on TV.com.
IlliGAL Blogging Push, PushGP, and Pushpop Introduction Push is a programming language designed for evolutionary computation, to be used as the programming language within which evolving programs are expressed. A concise introduction to the most recent standardized version of the language ("Push3") is contained in The Push 3.0 Programming Language Description. PushGP is a genetic programming system that evolves programs in the Push programming language. Multiple data types without constraints on code generation or manipulation. The 2002 article in the journal Genetic Programming and Evolvable Machines provides an introduction to the general principles and philosophy of the Push project, although that article was based on Push1 and one should therefore subsequently read the GECCO-2005 paper introducing the major new features of Push3. A variety of Push-based evolutionary computation sytems other than PushGP have been developed, including several (e.g. There is a Push email list and a new Push project blog. Older versions: Push2:
Memetics - Wikipedia, the free encyclopedi This article is related to the study of self-replicating units of culture, not to be confused with Mimesis. Memetics is a theory of mental content based on an analogy with Darwinian evolution, originating from the popularization of Richard Dawkins' 1976 book The Selfish Gene.[1] Proponents describe memetics as an approach to evolutionary models of cultural information transfer. The meme, analogous to a gene, was conceived as a "unit of culture" (an idea, belief, pattern of behaviour, etc.) which is "hosted" in the minds of one or more individuals, and which can reproduce itself, thereby jumping from mind to mind. Thus what would otherwise be regarded as one individual influencing another to adopt a belief is seen as an idea-replicator reproducing itself in a new host. As with genetics, particularly under a Dawkinsian interpretation, a meme's success may be due to its contribution to the effectiveness of its host. History[edit] The modern memetics movement dates from the mid-1980s.
The Blind Watchmaker - Wikipedia, the free The Blind Watchmaker: Why the Evidence of Evolution Reveals a Universe without Design is a 1986 book by Richard Dawkins in which he presents an explanation of, and argument for, the theory of evolution by means of natural selection. He also presents arguments to refute certain criticisms made on his previous book, The Selfish Gene. (Both books espouse the gene-centric view of evolution.) An unabridged audiobook edition was released by Audible Inc in 2011, narrated by Richard Dawkins and Lalla Ward. Overview[edit] To dispel the idea that complexity cannot arise without the intervention of a "creator", Dawkins uses the example of the eye. In developing his argument that natural selection can explain the complex adaptations of organisms, Dawkins' first concern is to illustrate the difference between the potential for the development of complexity as a result of pure randomness, as opposed to that of randomness coupled with cumulative selection. Notes[edit] References[edit]
jom-emit/overview.html Journal of Memetics -Evolutionary Models of Information Transm Back to JoM-EMIT Home The History of the Memetic Approach At least since the early seventies several authors have tried to adopt the principle of evolution by selection to understand the continuous change in cultural behaviors (Boyd [1], Calvin [2], Campbel [6], Cloak [7]). Richard Dawkins popularized the memetic approach. Memetics and Related Evolutionary Approaches We see the memetic approach as an evolutionary one. Evolutionary theories are applied in a wide variety of disciplines. We feel that this plethora of approaches proves the potential of evolutionary thought in all fields of human sciences. Key References (for more see the Bibliography of Memetics) Boyd R. and Richerson PJ. 1985. Back to JoM-EMIT Home
karma.med.harvard.edu/wiki/Digital_evoluti... From FreeBio Digital Evolution Rich Lenski decided he was onto a good thing from his very first encounter with digital evolution. It all began when he used the technology in which artificial organisms in the form of computer code evolve independently by self-replicating, mutating, and competing to re-examine an earlier study with bacteria. The original study had contradicted ‘some influential theory’ suggesting that random mutations show a systematic tendency towards synergistic interactions. His digital results, he discovered, matched his organic ones. ‘It's great when these two powerful experimental systems agree, because it suggests some generality about the evolution of genetic architectures', recalls Lenski, professor of microbial ecology at Michigan State University (MSU). Complex Challenges and the Virtue of Simplicity He can hardly contain himself. Figure 1. Which is what tempted Lenski and Adami to examine the challenge in their virtual world. Evolution in Action Further Reading
Human Based Genetic Algorithm Alexander Kosorukoff alex<at>3form.com Abstract Genetic algorithms (GA) are search procedures learned from Nature and based on mechanics of natural selection and genetics. In this paper a new kind of GA is presented. This paper contains description of Human Based Genetic Algorithm (HBGA), its relationship with other known types of evolutionary computation and creativity techniques, the results of its usage for the purpose of collaborative web-based problem solving, and conclusions about using genetic algorithms as engines of innovation and creativity in corporations and non-profit organizations. The paper is organized into the following parts: overview of related work , general description of organizational evolutionary methods including HBGA and its web application , results and conclusions . Every new idea is a recombination of existing ideas. Research related to HBGA can be divided into two general areas: Evolutionary computation Small introduction to genetic algorithms Brainstorming Resume
Moshe Sipper, The Artificial Self-Replicat ... living organisms are very complicated aggregations of elementary parts, and by any reasonable theory of probability or thermodynamics highly improbable. That they should occur in the world at all is a miracle of the first magnitude; the only thing which removes, or mitigates, this miracle is that they reproduce themselves. Therefore, if by any peculiar accident there should ever be one of them, from there on the rules of probability do not apply, and there will be many of them, at least if the milieu is reasonable. John von Neumann, Theory of Self-Reproducing Automata. In the late 1940's eminent mathematician and physicist John von Neumann had become interested in the question of whether a machine can self-replicate, that is, produce copies of itself. The study of artificial self-replicating structures or machines has been taking place now for almost half a century. One of the central models used to study self-replication is that of cellular automata (CA). General references
genetic-programming.com-Home-Page Evolutionary Computation and its application to art and design by Craig Reynolds is the general term for several computational techniques which are based to some degree on the evolution of biological life in the natural world. My work in evolutionary computation has related to . I've used evolutionary systems to create behavior control programs for artificial agents. These evolved behavior relate to steering around a simulated environment. The most widely used form of evolutionary computation are Genetic Algorithms . I'm especially interested in the use of evolutionary techniques to discover controllers for animated motion of real or virtual objects: Karl Sims has evolved delightful virtual creatures based on their locomotion skills, and through coevolution has created others that play a certain wrestling game. Larry Gritz ( old ) used Genetic Programming to generate controllers for the animation of physically based articulated figures, such as a jumping desk lamp and a three segment arm. Henrik Hautop Lund : Ms.
Human Based Genetic Algorithm Alexander Kosorukoff alex<at>3form.com Abstract Genetic algorithms (GA) are search procedures learned from Nature and based on mechanics of natural selection and genetics. In this paper a new kind of GA is presented. This paper contains description of Human Based Genetic Algorithm (HBGA), its relationship with other known types of evolutionary computation and creativity techniques, the results of its usage for the purpose of collaborative web-based problem solving, and conclusions about using genetic algorithms as engines of innovation and creativity in corporations and non-profit organizations. The paper is organized into the following parts: overview of related work , general description of organizational evolutionary methods including HBGA and its web application , results and conclusions . Every new idea is a recombination of existing ideas. Research related to HBGA can be divided into two general areas: Evolutionary computation Small introduction to genetic algorithms Brainstorming Resume
Interactive Evolutionary Structure It has been a while since bottom-up design methodology became a major research field in system science. In a bottom-up system, complex behavior as a whole, which is more than the sum of the parts, emerges from aggrigation of components. As shown in cell-automata and Boids, those systems are of capital interest because of the qualitative disparity between the simplicity of the system and complex phenomena emmerging from there. However, it is difficult for a user to interfere with the system's overall behavior because of the essential inability to take an analytical process to predict the behavior and severe parameter coordination to adjust it. Most of the systems, however it is an important step to open up a whole new field, tend to be a subject of an objectless experimentation where a user blindly changes to see and enjoy a result. This research incorporates evolutionary approach to allow a bottom-up system to have an elasticity to change itself to fit a user's expectation.