Strengths of GA

• The genetic algorithm is the most robust and intrinsically parallel
  
• They can explore the solution space in multiple directions at once. If one path turns out to be a dead end, they can easily eliminate it and continue work on more promising avenues, giving them a greater chance each run of finding the optimal solution.
  
  
• A GA that explicitly evaluates a small number of individuals is implicitly evaluating a much larger group of individuals.
  
• In a linear problem, the fitness of each component is independent, so any improvement to any one part will result in an improvement of the system as a whole.
  
• Nonlinearity is the norm, where changing one component may have ripple effects on the entire system, and where multiple changes that individually are detrimental may lead to much greater improvements in fitness when combined.
  
• The lack of rigidity of the method makes it powerful, while its fundamental operators make it effective. i.e., GA is highly flexible.
  
• The search ability of the genetic algorithm is dictated by two operators. The mutation operator introduces new information into the population. The crossover operator sorts through this information, producing new elements with different ways of combining this information.
  
• They perform well in problems for which the fitness landscape is complex ones where the fitness function is discontinuous, noisy, changes over time, or has many local optima.
  

Related Posts:

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

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