This presentation discusses about Genetic algorithms vs Traditional algorithms

1. Department of Information Technology 1Soft Computing (ITC4256 )
Dr. C.V. Suresh Babu
Professor
Department of IT
Hindustan Institute of Science & Technology
Genetic Algorithms vs. Traditional
Algorithms

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Definition
Genetic algorithm is an algorithm for solving both constrained
and unconstrained optimization problems that are based on
Genetics and Natural Selection
while
traditional algorithm is an unambiguous specification that
defines how to solve a problem. Thus, this is the
main difference between genetic algorithm
and traditional algorithm.

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Usage
The specific use of each algorithm is an important difference
between genetic algorithm and traditional algorithm. That is;
the genetic algorithm helps to find the optimal solutions for
difficult problems
while
traditional algorithm provides a step by step methodical
procedure to solve a problem.

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Complexity
Another difference between genetic algorithm
and traditional algorithm is that a genetic
algorithm is more advanced than a traditional
algorithm.

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Applications
Genetic Algorithm is used in fields such as research,
Machine Learning and, Artificial Intelligence. Traditional
algorithm is used in fields such as Programming,
Mathematics, etc.
Hence, this is also an important difference
between genetic algorithm and traditional algorithm.

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• GA's work with string coding of variables instead of variables.so
that coding discretising the search space even though the function
is continuous.
• GA's work with population of points instead of single point.
• In GA's previously found good information is emphasized using
reproduction operator and propagated adaptively through
crossover and mutation operators.
• GA does not require any auxiliary information except the objective
function values.
• GA uses the probabilities in their operators.
This nature of narrowing the search space as the search progresses ,is adaptive and is the unique
characteristic of Genetic Algorithms.
GA are radially different from traditional optimization methods

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A paper review
Comparison of Genetic Programming