Genetic Algorithms

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Genetic Algorithms

Models that optimize rules by mimicking the Darwinian Law of survival of the fittest. A set of rules is chosen from those that work the best. The weakest are discarded. In addition, two successful rules can be combined (the equivalent to genetic cross-overs) to produce offspring rules. The offspring can replace the parents, or they will be discarded if less successful than the parents. Mutation is also accomplished by randomly changing elements. Mutation and cross-over occur with low probability, as in nature.
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Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm.
In order to accelerate the evolution rate and prevent the premature of the genetic algorithm, we integrate the Tabu search algorithm into the genetic algorithm.
Keywords: Systems Engineering, Analysis of Alternatives (AoA), Milestone B, Unmanned Undersea Vehicle (UUV), Genetic Algorithms, Systems Engineering Concept Tool and Method (SECTM)
However, sometimes an architect may just want to express that a certain pattern suggested by the genetic algorithm in a certain context is not appropriate.
The investigation was held using different values for the parameters used in the genetic algorithm to find those which can give better results.
In: Genetic Algorithms and their Applications: Proceedings of the School International Conference on Genetic Algorithms, 108-115.
Genetic algorithm is used to evolve the population of initial solution into optimal solution.
A genetic algorithm is then used in order to determine the best combination of information extracted by the selected criterion.
Even powerful supercomputers would take many years to sequentially crunch through every possible combination involved; but genetic algorithms offer a more practical and human solution.
The results presented in this article are part of the solution VEGA research project titled Implementation of genetic algorithms into optimization of technological processes.
1: Outline of a typical pseudo PASCAL form of the single genetic algorithm.
This paper will focus in the next sections on applying more than the genetic algorithm to optimize the chosen locations, with comparisons between those algorithms and Mobinil points.

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