Evolutionary programming using a mixed mutation strategy.

*(English)*Zbl 1142.68469Summary: Different mutation operators have been proposed in evolutionary programming, but for each operator there are some types of optimization problems that cannot be solved efficiently. A mixed strategy, integrating several mutation operators into a single algorithm, can overcome this problem. Inspired by evolutionary game theory, this paper presents a mixed strategy evolutionary programming algorithm that employs the Gaussian, Cauchy, Lévy, and single-point mutation operators. The novel algorithm is tested on a set of 22 benchmark problems. The results show that the mixed strategy performs equally well or better than the best of the four pure strategies does, for all of the benchmark problems.

##### MSC:

68T05 | Learning and adaptive systems in artificial intelligence |

68T20 | Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) |

68W20 | Randomized algorithms |

##### Keywords:

design of algorithms; randomized algorithms; global optimization; evolutionary programming; mixed strategy
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##### References:

[1] | Baeck, T.; Schwefel, H.P., An overview of evolutionary algorithms for parameter optimization, Evolutionary computation, 1, 1, 1-23, (1993) |

[2] | Chellapilla, K., Combining mutation operators in evolutionary programming, IEEE transactions of evolutionary computation, 2, 3, 91-96, (1998) |

[3] | Choi, D.H.; Oh, S.Y., A new mutation rule for evolutionary programming motivated from backpropagation learning, IEEE transactions of evolutionary computation, 4, 2, 188-190, (2000) |

[4] | Eiben, A.E.; Hinterding, R.; Michalewicz, Z., Parameter control in evolutionary algorithms, IEEE transactions of evolutionary computation, 3, 2, 124-141, (1999) |

[5] | Fogel, D.B., Evolution computation: towards a new philosophy of machine intelligence, (1995), IEEE Press Piscataway, NJ |

[6] | () |

[7] | Fogel, L.J.; Owens, A.J.; Walsh, M.J., Artificial intelligence through simulated evolution, (1966), Wiley New York · Zbl 0148.40701 |

[8] | Goldberg, D.E., Genetic algorithms in search, optimization and machine learning, (1989), Addison-Wesley Reading, MA · Zbl 0721.68056 |

[9] | He, J.; Yao, X., A game-theoretic approach for designing mixed mutation strategies, (), 279-288 |

[10] | Holland, J.H., Adaptation in natural and artificial system: an introductory analysis with application to biology, control, and artificial intelligence, (1992), MIT Press Cambridge, MA |

[11] | Iwamatsu, M., Generalized evolutionary programming with Lévy-type mutation, Computer physics computation, 147, 8, 729-732, (2002) · Zbl 1002.65066 |

[12] | Ji, M.; Tang, H.; Guo, J., A single-point mutation evolutionary programming, Information processing letters, 90, 2, 293-299, (2004) · Zbl 1176.90442 |

[13] | Lee, C.Y.; Yao, X., Evolutionary programming using mutations based on the Lévy probability distribution, IEEE transactions of evolutionary computation, 8, 2, 1-13, (2004) |

[14] | Schwefel, H.P., Numerical optimization of computer models, (1981), John Wiley and Sons New York |

[15] | Swain, A.K.; Morris, A.S., Performance improvement of self-adaptive evolutionary methods with a dynamic lower bound, Information processing letters, 82, 1, 55-63, (2002) · Zbl 1013.68288 |

[16] | Weibull, J.W., Evolutionary game theory, (1995), MIT press Cambridge, MA · Zbl 0879.90206 |

[17] | Yao, X.; Liu, Y.; Lin, G., Evolutionary programming made faster, IEEE transactions of evolutionary computation, 3, 2, 82-102, (1999) |

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