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.
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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).
7.
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
