Genetic algorithms are a type of optimization technique inspired by natural selection and genetics. They are commonly used to find optimal or near-optimal solutions to complex problems by generating populations of solutions and using biologically inspired operators like mutation and crossover to select new solutions. Genetic algorithms maintain a population of potential solutions and evolve them over generations to find better solutions to optimization and search problems.

1. by:
A.Bhuvaneshwari,
M.SC(CS)
From:NSCAS

2.  Genetic Algorithm (GA) is a search-based
optimization technique based on the principles
of Genetics and Natural Selection.
 It is frequently used to find optimal or near-
optimal solutions to difficult problems which
otherwise would take a lifetime to solve.
 It is frequently used to solve optimization
problems, in research, and in machine learning.

3. The algorithm begins by
random initial population.
The algorithm then
creates a sequence of new
populations. At each step,
the algorithm uses the
individuals in the current
generation to create the
next population.
 :  DIAGRAM:

4.  Genetic algorithms are commonly used to generate
high-quality solutions to optimization and search
problems by relying on biologically inspired operators
such as mutation, crossover and selection.
 They are Robust
 Provide optimization over large space state.
 Unlike traditional AI, they do not break on slight
change in input or presence of noise

5.  Is faster and more efficient as compared to the
traditional methods.
 Has very good parallel capabilities.
 Provides a list of “good” solutions and not just a single
solution.
 Always gets an answer to the problem, which gets
better over the time.

6.  The population of individuals are maintained within
search space. Each individual represent a solution in
search space for given problem.
 Each individual is coded as a finite length vector
(analogous to chromosome) of components.
 These variable components are analogous to Genes.
Thus a chromosome (individual) is composed of
several genes (variable components).

8.  Population − It is a subset of all the possible (encoded)
solutions to the given problem. The population for a GA is
analogous to the population for human beings except that
instead of human beings, we have Candidate Solutions
representing human beings.
 Chromosomes − A chromosome is one such solution to
the given problem.
 Gene − A gene is one element position of a chromosome.
 Allele − It is the value a gene takes for a particular
chromosome.

9.  Genetic algorithms have many applications, some of
them are –
 Recurrent Neural Network
 Mutation testing
 Code breaking
 Filtering and signal processing
 Learning fuzzy rule base etc