How GA differs from other optimization techniques?

1. They work with a coding of the parameter set, not the parameters themselves.
   
2. They search from a population of points in the problem domain, not a singular point.
   
3. They use payoff information as the objective function rather than derivatives of the problem or auxiliary knowledge.
   
   
4. They utilize probabilistic transition rules based on fitness rather than deterministic one.
   
5. It can quickly scan a vast solution set.
   
6. Bad proposals do not affect the end solution negatively as they are simply discarded.
   
7. The inductive nature of the GA means that it doesn't have to know any rules of the problem - it works by its own internal rules. This is very useful for complex or loosely defined problems.
   
8. While the great advantage of GA is the fact that they find a solution through evolution, this is also the biggest disadvantage. Evolution is inductive; in nature life does not evolve towards a good solution, it evolves away from bad circumstances. This can cause a species to evolve into an evolutionary dead end.
   

References
“Genetic Algorithms”, Burhaneddin SANDIKCI, http://www.ie.bilkent.edu.tr/~lors/ie572/burhaneddin_html/IE572_GA.html
Genetic Algorithms, http://www.tjhsst.edu/~ai/AI2001/GA.HTM
Related Posts:

• ABSTRACT
• INTRODUCTION
• JOB SHOP SCHEDULING
• Search Methods and Optimization Techniques
• Genetic Algorithm
• Mechanism
• Characteristics of GA

Table of Contents
© 2006 Kumaravel & Project Team

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