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Genetic algorithms vs Traditional algorithms
Genetic algorithms and traditional algorithms differ in their definitions, usages, and complexity. Genetic algorithms are based on genetics and natural selection, and help find optimal solutions to difficult problems. They are more advanced than traditional algorithms which provide step-by-step procedures. Genetic algorithms are used in fields like machine learning and artificial intelligence, while traditional algorithms are used in programming and mathematics.
Genetic algorithms vs Traditional algorithms
- 1. Department of InformationTechnology 1Soft Computing (ITC4256 ) Dr. C.V. Suresh Babu Professor Department of IT Hindustan Institute of Science & Technology Genetic Algorithms vs. Traditional Algorithms
- 2. Department of InformationTechnology 2Soft Computing (ITC4256 ) 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.
- 3. Department of InformationTechnology 3Soft Computing (ITC4256 ) 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.
- 4. Department of InformationTechnology 4Soft Computing (ITC4256 ) Complexity Another difference between genetic algorithm and traditional algorithm is that a genetic algorithm is more advanced than a traditional algorithm.
- 5. Department of InformationTechnology 5Soft Computing (ITC4256 ) 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.
- 6. Department of InformationTechnology 6Soft Computing (ITC4256 ) • 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
- 7. Department of InformationTechnology 7Soft Computing (ITC4256 )
- 8. Department of InformationTechnology 8Soft Computing (ITC4256 ) A paper review Comparison of Genetic Programming