Tag: Genetic Algorithm Optimization

Genetic Algorithm in AI | Operators | Working

Genetic Algorithm-


In Artificial Intelligence,

  • Genetic Algorithm is one of the heuristic algorithms.
  • They are used to solve optimization problems.
  • They are inspired by Darwin’s Theory of Evolution.
  • They are an intelligent exploitation of a random search.
  • Although randomized, Genetic Algorithms are by no means random.




Genetic Algorithm works in the following steps-




  • Randomly generate a set of possible solutions to a problem.
  • Represent each solution as a fixed length character string.




Using a fitness function, test each possible solution against the problem to evaluate them.




  • Keep the best solutions.
  • Use best solutions to generate new possible solutions.




Repeat the previous two steps until-

  • Either an acceptable solution is found
  • Or until the algorithm has completed its iterations through a given number of cycles / generations.


Basic Operators-


The basic operators of Genetic Algorithm are-


1. Selection (Reproduction)-


  • It is the first operator applied on the population.
  • It selects the chromosomes from the population of parents to cross over and produce offspring.
  • It is based on evolution theory of “Survival of the fittest” given by Darwin.


There are many techniques for reproduction or selection operator such as-

  • Tournament selection
  • Ranked position selection
  • Steady state selection etc.


2. Cross Over-


  • Population gets enriched with better individuals after reproduction phase.
  • Then crossover operator is applied to the mating pool to create better strings.
  • Crossover operator makes clones of good strings but does not create new ones.
  • By recombining good individuals, the process is likely to create even better individuals.


3. Mutation-


  • Mutation is a background operator.
  • Mutation of a bit includes flipping it by changing 0 to 1 and vice-versa.
  • After crossover, the mutation operator subjects the strings to mutation.
  • It facilitates a sudden change in a gene within a chromosome.
  • Thus, it allows the algorithm to see for the solution far away from the current ones.
  • It guarantees that the search algorithm is not trapped on a local optimum.
  • Its purpose is to prevent premature convergence and maintain diversity within the population.


Flow Chart-


The following flowchart represents how a genetic algorithm works-





Genetic Algorithms offer the following advantages-




  • Genetic Algorithms are better than conventional AI.
  • This is because they are more robust.




  • They do not break easily unlike older AI systems.
  • They do not break easily even in the presence of reasonable noise or if the inputs get change slightly.




While performing search in multi modal state-space or large state-space,

  • Genetic algorithms has significant benefits over other typical search optimization techniques.


To gain better understanding about Genetic Algorithm & its Working,

Watch this Video Lecture


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