nsga2R | R Documentation |

A fast and elitist multiobjective genetic algorithm based on R.

nsga2R(fn, varNo, objDim, lowerBounds = rep(-Inf, varNo), upperBounds = rep(Inf, varNo), popSize = 100, tourSize = 2, generations = 20, cprob = 0.7, XoverDistIdx = 5, mprob = 0.2, MuDistIdx = 10)

`fn` |
Objective functions to be minimized |

`varNo` |
Number of decision variables |

`objDim` |
Number of objective functions |

`lowerBounds` |
Lower bounds of each decision variable |

`upperBounds` |
Upper bounds of each decision variable |

`popSize` |
Size of population |

`tourSize` |
Size of tournament |

`generations` |
Number of generations |

`cprob` |
Crossover probability |

`XoverDistIdx` |
Crossover distribution index, it can be any nonnegative real number |

`mprob` |
Mutation probability |

`MuDistIdx` |
Mutation distribution index, it can be any nonnegative real number |

The returned value is a 'nsga2R' object with the following fields in additional to above NSGA-II settings:

`parameters` |
Solutions of decision variables found |

`objectives` |
Non-dominated objective function values |

`paretoFrontRank` |
Nondomination ranks (or levels) that each non-dominated solution belongs to |

`crowdingDistance` |
Crowding distance of each non-dominated solution |

Ching-Shih (Vince) Tsou cstsou@mail.ntcb.edu.tw

Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002), " A fast and elitist multiobjective genetic algorithm: NSGA-II", *IEEE Transactions on Evolutionary Computation*, **6(2)**, 182-197.

# find the non-dominated solutions of zdt3 test problem results <- nsga2R(fn=zdt3, varNo=30, objDim=2, lowerBounds=rep(0,30), upperBounds=rep(1,30), popSize=50, tourSize=2, generations=50, cprob=0.9, XoverDistIdx=20, mprob=0.1,MuDistIdx=3) plot(results$objectives)

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