Genetic Algorithm ?

• 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.

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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.

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.