Advantages of Genetic Algorithm

• • The parallel capabilities of genetic algorithms are best.
• • It helps in optimizing various problems such as discrete functions, multi-objective problems, and continuous functions.
• • It provides a solution for a problem that improves over time.
• • A genetic algorithm does not need derivative information.

Limitations of Genetic Algorithms

• • Genetic algorithms are not efficient algorithms for solving simple problems.
• • It does not guarantee the quality of the final solution to a problem.
• • Repetitive calculation of fitness values may generate some computational challenges.

Difference between Genetic Algorithms and Traditional Algorithms

• • A search space is the set of all possible solutions to the problem. In the traditional algorithm, only one set of solutions is maintained, whereas, in a genetic algorithm, several sets of solutions in search space can be used.
• • Traditional algorithms need more information in order to perform a search, whereas genetic algorithms need only one objective function to calculate the fitness of an individual.
• • Traditional Algorithms cannot work parallelly, whereas genetic Algorithms can work parallelly (calculating the fitness of the individualities are independent).
• • One big difference in genetic Algorithms is that rather of operating directly on seeker results, inheritable algorithms operate on their representations (or rendering), frequently appertained to as chromosomes.
• • One of the big differences between traditional algorithm and genetic algorithm is that it does not directly operate on candidate solutions.
• • Traditional Algorithms can only generate one result in the end, whereas Genetic Algorithms can generate multiple optimal results from different generations.
• • The traditional algorithm is not more likely to generate optimal results, whereas Genetic algorithms do not guarantee to generate optimal global results, but also there is a great possibility of getting the optimal result for a problem as it uses genetic operators such as Crossover and Mutation.
• • Traditional algorithms are deterministic in nature, whereas Genetic algorithms are probabilistic and stochastic in nature.