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  • Ramani Aswath

    Member • Oct 25, 2013

    It is a AI process that mimics the process of Darwin's Natural Selection.

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    <a href="https://www.obitko.com/tutorials/genetic-algorithms/ga-basic-description.php" target="_blank" rel="nofollow noopener noreferrer">Genetic Algorithm Description - Introduction to Genetic Algorithms - Tutorial with Interactive Java Applets</a>


    <a href="https://lancet.mit.edu/mbwall/presentations/IntroToGAs/" target="_blank" rel="nofollow noopener noreferrer">lancet.mit.edu</a>

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  • lovebox

    Member • Oct 25, 2013

    Genetic Algorithm is an Evolutionary Algorithm that works in the same way as the Biological Natural Selection process.
    It is used in search and optimisation.
    • Genetic algorithm is applied to a possible set of solutions of a given problem. These possible solutions are selected randomly.
    • Individual elements of this set are then tested by employing fitness functions. In this way, each possible solution is assigned a fitness value.
    • After this, a certain number of solutions with the highest fitness values are selected and this forms a new set of possible values.
    • Sometimes the possible solutions with highest fitness values from a different set are also combined with an existing set of values and the evaluation is done repetitively. This increases the average fitness of each successor set of solutions as compared to its predecessor set.
    • The process finally terminates and gives the final output when any further iterations do not produce better results.
    Its applications are fairly diverse and include robotics, operations research, genetics, bioinformatics, pharmaceutical chemistry, economics, etc.


    You may read: "Introduction to Genetic Algorithms" - Melanie Mitchell.
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  • Vinodhini.V

    Member • Oct 25, 2013

    lovebox
    Genetic Algorithm is an Evolutionary Algorithm that works in the same way as the Biological Natural Selection process.
    It is used in search and optimisation.
    • Genetic algorithm is applied to a possible set of solutions of a given problem. These possible solutions are selected randomly.
    • Individual elements of this set are then tested by employing fitness functions. In this way, each possible solution is assigned a fitness value.
    • After this, a certain number of solutions with the highest fitness values are selected and this forms a new set of possible values.
    • Sometimes the possible solutions with highest fitness values from a different set are also combined with an existing set of values and the evaluation is done repetitively. This increases the average fitness of each successor set of solutions as compared to its predecessor set.
    • The process finally terminates and gives the final output when any further iterations do not produce better results.
    Its applications are fairly diverse and include robotics, operations research, genetics, bioinformatics, pharmaceutical chemistry, economics, etc.


    You may read: "Introduction to Genetic Algorithms" - Melanie Mitchell.
    Yes i can get your point, what will be the complexity of this algorithm ?
    Are you sure? This action cannot be undone.
    Cancel

  • Vinodhini.V

    Member • Oct 25, 2013

    lovebox
    Genetic Algorithm is an Evolutionary Algorithm that works in the same way as the Biological Natural Selection process.
    It is used in search and optimisation.
    • Genetic algorithm is applied to a possible set of solutions of a given problem. These possible solutions are selected randomly.
    • Individual elements of this set are then tested by employing fitness functions. In this way, each possible solution is assigned a fitness value.
    • After this, a certain number of solutions with the highest fitness values are selected and this forms a new set of possible values.
    • Sometimes the possible solutions with highest fitness values from a different set are also combined with an existing set of values and the evaluation is done repetitively. This increases the average fitness of each successor set of solutions as compared to its predecessor set.
    • The process finally terminates and gives the final output when any further iterations do not produce better results.
    Its applications are fairly diverse and include robotics, operations research, genetics, bioinformatics, pharmaceutical chemistry, economics, etc.


    You may read: "Introduction to Genetic Algorithms" - Melanie Mitchell.
    Yes i can get your point, what will be the complexity of this algorithm ?
    Are you sure? This action cannot be undone.
    Cancel

  • Vinodhini.V

    Member • Oct 25, 2013

    A.V.Ramani
    It is a AI process that mimics the process of Darwin's Natural Selection.

    #-Link-Snipped-#

    <a href="https://www.obitko.com/tutorials/genetic-algorithms/ga-basic-description.php" target="_blank" rel="nofollow noopener noreferrer">Genetic Algorithm Description - Introduction to Genetic Algorithms - Tutorial with Interactive Java Applets</a>


    <a href="https://lancet.mit.edu/mbwall/presentations/IntroToGAs/" target="_blank" rel="nofollow noopener noreferrer">lancet.mit.edu</a>

    #-Link-Snipped-#

    #-Link-Snipped-#
    Thank you for directing me sir 😀
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