Autonomous Genetic Algorithm for Functional Optimization

Genetic algorithm (GA) is effective for global optimizations, but needs the user to define several parameters.Unless these parameters are defined appropriately, search efficiency drops significantly.There are, however, no clear rules for the defining, and almost all users have considerable difficulty to use GA efficiently.A good algorithm must be use-friendly.It should not, if possible, need the user to define such parameters and can play high performance for any optimization problem.This paper proposes an autonomous GA addressing these problems.