Weakness of GA

Although genetic algorithms have proven to be an efficient and powerful problem-solving strategy, they are not a universal remedy. The following are some limitations of GA.

• The language used to specify candidate solutions must be robust; i.e., it must be able to tolerate random changes such that fatal errors or nonsense do not consistently result.
  
  
• The problem of how to write the fitness function must be carefully considered so that higher fitness is attainable and actually does equate to a better solution for the given problem. If the fitness function is chosen poorly or defined imprecisely, the genetic algorithm may be unable to find a solution to the problem, or may end up solving the wrong problem.
  
• In addition to making a good choice of fitness function, the other parameters of a GA, the size of the population, the rate of mutation and crossover, the type and strength of selection must be also chosen with care. If the population size is too small, the genetic algorithm may not explore enough of the solution space to consistently find good solutions. If the rate of genetic change is too high or the selection scheme is chosen poorly, beneficial schema may be disrupted and the population may enter error catastrophe, changing too fast for selection to ever bring about convergence
  
• One well-known problem that can occur with a GA is known as premature convergence. The combination of difficulties in both these operators can lead to premature convergence, and non-competitive results. If an individual that is more fit than most of its competitors emerges early on in the course of the run, it may reproduce so abundantly that it drives down the population's diversity too soon, leading the algorithm to converge on the local optimum that that individual represents rather than searching the fitness landscape thoroughly enough to find the global optimum.
  

Related Posts:

• ABSTRACT
• INTRODUCTION
• JOB SHOP SCHEDULING
• Search Methods and Optimization Techniques
• Genetic Algorithm

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