A Hybrid Nondominant-Based Genetic Algorithm (NSGA-II) for Multiobjective Optimization to Minimize Vibration Amplitude in the End Milling Process

<div>Performance of fitness value generation and the best and most consistent variable performances in structured form: (a) distance of individuals, (b) average spread of generations, (c) rank histogram of individuals, and (d) GA optimization tool interface.</div>

Advances in Materials Science and Engineering

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Figure 5

Figure 5: A Hybrid Nondominant-Based Genetic Algorithm (NSGA-II) for Multiobjective Optimization to Minimize Vibration Amplitude in the End Milling Process 

Figure 5 | A Hybrid Nondominant-Based Genetic Algorithm (NSGA-II) for Multiobjective Optimization to Minimize Vibration Amplitude in the End Milling Process 