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Mr. Genetic Algorithms Description Genetic algorithms “represent a powerful and
robust approach for developing heuristics for large-scale combinatorial ...
... as well as a basic description of how ... trees: Tree-shaped structures that represent
sets of ... Genetic algorithms: Optimization techniques that use processes such ...
... Genetic Programming Genetic programming is a technique ... is substantially more reliable
than previous algorithms. ... 7. Brief description of some advanced Expert ...
... might be a fuller description of the ... have been devised to represent and process ...
Artificial intelligence, data warehouses, genetic algorithms, neural networks ...
... of the "vague" sort is a good description of a ... in AI would people devise algorithms
literally dumber ... Genetic drift ought therefore to produce non-functional ...
Submitted by fokita on December 3, 2007
Category: Technology
Words: 2157 | Pages: 9
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Genetic Algorithms
Description
Genetic algorithms “represent a powerful and robust approach for developing heuristics for large-scale combinatorial optimisation problems” (The contribution of neural networks and genetic algorithms to business decision support-Academic myth or practical solution. Metaxiotis, Kostas & Psarras, John) This means that a genetic algorithm is a process that follows a set of instructions or parameters set and repeats itself until they find the more adequate, optimum solution. Nonetheless the process of doing so is much more complex.
The invention of Genetic Algorithms is attributed to John Holland, a professor at the University of Michigan. Nonetheless he had assistant from his colleagues and students at the university. The first investigations on the matters were carried away in the 1960s; it was not until 1975 were John Holland published the findings in the book Holland J.H., Adaptation in natural and artificial system, Ann Arbor, The University of Michigan Press, 1975. It has been suggested that J. Holland had two basic aims when developing this: “to improve the understanding of natural adaptation process and to design artificial systems having properties similar to natural systems” (Goldberg, David Genetic Algorithms, Addison Wesley, 1988.) The findings provided “the basic tools for the fabrication of reliable, robust, self-guided software codes” (Nuziale, Tony & Vagenas, Nick Genetic algorithms for reliability assessment of mining equipment) Genetic algorithms therefore represent a set of algorithms that follow the principles of natural selection. Thus it can be said that the fundamental idea behind Genetic Algorithms is the same idea behind Charles Darwin theory of evolution, survival of the fittest. Hence, before running the algorithms the definition of certain terms must be defined since they simulate certain biological aspects of the theory of evolution (for this please...
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