DISADVANTAGES AND LIMITATIONS OF GENETIC ALGORITHM

DISADVANTAGES AND LIMITATIONS OF GENETIC ALGORITHM

1. The problems occurs identifying fitness function
2. Definition of representation for the problem
3. The problem of choosing the various parameters like the size of the population, mutation rate, cross over rate, the selection method and its strength.
4. Premature convergence occurs. [4]
5. Cannot easily incorporate problem specific information
6. Not good at identifying local optima
7. No effective terminator.
8. Not effective for smooth unimodal functions
9. Needs to be coupled with a local search technique.
10. Require large number of response (fitness) function evaluations
11. Configuration is not straightforward
12. Cannot use gradients.[5]
13. Not all problems can be framed in the mathematical manner that genetic algorithms demand
14. Development of a genetic algorithm and interpretation of the results requires an expert who has both the programming and statistical/mathematical skills demanded.
15. Most genetic algorithms rely on random number generators that produce different results each time the model runs. Although there is likely to be a high degree of consistency among the runs, they may vary. [6]