## Genetic Algorithms + Data Structures = Evolution ProgramsGenetic algorithms are founded upon the principle of evolution, i.e., survival of the fittest. Hence evolution programming techniques, based on genetic algorithms, are applicable to many hard optimization problems, such as optimization of functions with linear and nonlinear constraints, the traveling salesman problem, and problems of scheduling, partitioning, and control. The importance of these techniques is still growing, since evolution programs are parallel in nature, and parallelism is one of the most promising directions in computer science. The book is self-contained and the only prerequisite is basic undergraduate mathematics. This third edition has been substantially revised and extended by three new chapters and by additional appendices containing working material to cover recent developments and a change in the perception of evolutionary computation. |

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### Contents

1 | |

What Are They? | 13 |

How Do They Work? 33 | 32 |

Why Do They Work? | 45 |

Selected Topics | 57 |

Binary or Float? | 97 |

Fine Local Tuning | 107 |

Handling Constraints | 121 |

The Traveling Salesman Problem | 209 |

Evolution Programs for Various Discrete Problems | 239 |

Machine Learning | 267 |

Evolutionary Programming and Genetic Programming 283 | 282 |

A Hierarchy of Evolution Programs | 289 |

Evolution Programs and Heuristics | 307 |

Conclusions 329 | 328 |

Appendix B | 349 |

### Other editions - View all

Genetic Algorithms + Data Structures = Evolution Programs Z. Michalewicz,Zbigniew Michalewicz Limited preview - 1996 |

Genetic Algorithms + Data Structures = Evolution Programs Zbigniew Michalewicz No preview available - 2014 |

Genetic Algorithms + Data Structures = Evolution Programs Zbigniew Michalewicz No preview available - 2011 |

### Common terms and phrases

applied approach average best individual binary string bits Chapter chromosome chromosome representation contractive mapping convergence cost crossover crossover operator data structures decoders defined discussed domain edges encoding eval evaluation function evolution process evolution strategies evolutionary algorithms evolutionary computation evolutionary programming evolutionary techniques example experiments feasible and infeasible feasible solution floating point function f GAMS GAVaPS gene genetic algorithm genetic operators GENETIC-2 GENOCOP global optimum graph heuristic implementation incorporate infeasible individuals infeasible solutions initial population integer iteration knapsack linear constraints matrix method Michalewicz minimize mutation mutation operator nodes numerical optimization objective function offspring optimization problems parameters parents path penalty functions performance pop-size potential solutions probability procedure random number randomly recombination repair algorithms represents robot schema schemata search space selection sequence simulated annealing solution vector solve subtours tion tour transportation problem traveling salesman problem variables