John Holland introduced genetic algorithms in 1960 based on Darwin's theory of evolution. Genetic algorithms use operations inspired by biological evolution like mutation, crossover and selection to find high-quality solutions to optimization and search problems. The genetic algorithm applies natural selection to populations of candidate solutions to problems in order to evolve better solutions over generations.

1. Genetic Algorithm
Author: Sayyed Samiyullaha

2. Genetic Algorithm
John Holland introduced genetic algorithms in 1960
based on the concept of Darwin’s theory of
evolution.
Author: Sayyed Samiyullaha

3. Genetic Algorithm
The genetic algorithm is a method for solving both
constrained and unconstrained optimization problems
that is based on natural selection, the process that
drives biological evolution.
Author: Sayyed Samiyullaha

4. Genetic Algorithm
The genetic algorithm to solve a variety of
optimization problems that are not well
suited for standard optimization algorithms,
including problems in which the objective
function is discontinuous, stochastic, or
highly nonlinear. The genetic algorithm can
address problems of mixed integer
programming, where some components are
restricted to be integer-valued.
Author: Sayyed Samiyullaha

5. Genetic Algorithm
Genetic algorithms are commonly used to generate
high-quality solutions to optimization and search
problems by relying on bio-inspired operators such
as mutation, crossover and selection.
Author: Sayyed Samiyullaha

6. Genetic Algorithm
The genetic algorithm uses three main types
of rules at each step to create the next
generation from the current population:
 Selection rules
 Crossover rules
 Mutation rules
Author: Sayyed Samiyullaha