In system identification, the model identi- fied by least square method is adopted as an evolution tutor who selects the individuals having balanced performances in all objec- tives as elitists. In controller optimization, the velocity regulating capability required by AGV path tracking is employed as deci- sion-making preferences. This book presents a multi-objective genetic algorithm (MOGA) with Pareto optimality and elitist tactics for the control system design of automated guid- ed vehicle (AGV).

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