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Microcap & Penny Stocks : AREE - Formerly TVSI -- Ignore unavailable to you. Want to Upgrade?

To: Tommy Hicks who wrote (6037)	12/2/2000 12:00:14 AM
From: jmhollen	Read Replies (2) \| Respond to of 6528

neural network Artificial Intelligence • a computational network, often for pattern recognition, composed of mathematically defined elements that are thought to approximate the working of biological neurons; often composed of a layer that receives and organizes inputs, a hidden layer, and an output layer in which individual neurons identify particular patterns. Networks can be trained by back-propagation. ********************** .What are Genetic Algorithms? Genetic Algorithms are nondeterministic stochastic search/optimization methods that utilize the theories of evolution and natural selection to solve a problem within a complex solution space. They are computer-based problem solving systems which use computational models of some of the known mechanisms in evolution as key elements in their design and implementation. They are a member of a wider population of algorithm, Evolutionary Algorithms(EA). The major classes of EAs are: GENETIC ALGORITHMs, EVOLUTIONARY PROGRAMMING, EVOLUTION STRATEGIEs, CLASSIFIER SYSTEM, and GENETIC PROGRAMMING. They all share a common conceptual base of simulating the evolution of individual structures via processes of selection, mutation, and reproduction. The processes depend on the perceived performance of the individual structures as defined by an environment. EAs maintain a population of structures, that evolve according to rules of selection and other operators, that are referred to as "search operators", (or genetic operators), such as recombination and mutation. Each individual in the population receives a measure of it's fitness in the environment. Reproduction focuses attention on high fitness individuals, thus exploiting the available fitness information. Recombination and mutation perturb those individuals, providing general heuristics for exploration. Although simplistic from a biologist's viewpoint, these algorithms are sufficiently complex to provide robust and powerful adaptive search mechanisms. GAs differ from conventional optimization/search procedures in that: 1. They work with a coding of the parameter set, not the parameters themselves. 2. They search from a population of points in the problem domain, not a singular point. 3. They use a payoff information as the objective function rather than derivatives of the problem or auxiliary knowledge. 4. They utilize probabilistic transition rules based on fitness rather than deterministic one. Now, I write code for PLCs (programmable logic controllers) and some Basic and Visula Basic stuff, which controls machine and process functions (aka: SCADA) - but the noodle-gook above is where the brain trusts and think-tanks are mucking about. The end result will hopefully be products like StarTrek's "..Data..", or the infusion of some of those "..bionic techniques.." to help people who are currently disabled and/or non-fuctional. John :-) ps: I'm still look forward to the price-rise and impending invasion of "..Goin' to da Moon.." stock experts..!! :-)