## Applications of Multi-objective Evolutionary AlgorithmsThis book presents an extensive variety of multi-objective problems across diverse disciplines, along with statistical solutions using multi-objective evolutionary algorithms (MOEAs). The topics discussed serve to promote a wider understanding as well as the use of MOEAs, the aim being to find good solutions for high-dimensional real-world design applications. The book contains a large collection of MOEA applications from many researchers, and thus provides the practitioner with detailed algorithmic direction to achieve good results in their selected problem domain. |

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### Contents

An Introduction to MultiObjective Evolutionary | 1 |

Applications of MultiObjective Evolutionary | 29 |

References | 52 |

Heating of a NonFerromagnetic Strip | 69 |

A Case Study | 79 |

References | 98 |

Acknowledgements | 122 |

Automatic Control System Design via | 155 |

On Machine Learning with Multiobjective | 393 |

A Method | 427 |

MultiObjective Evolutionary Algorithms | 451 |

Design of Fluid Power System Using a Multi | 483 |

Elimination of Exceptional Elements in Cellular | 505 |

SingleObjective and MultiObjective Evolutionary | 529 |

Evolutionary Operators Based on Elite Solutions | 555 |

MultiObjective Rectangular Packing Problem | 581 |

References | 174 |

The Use of Evolutionary Algorithms to Solve | 177 |

Acknowledgments | 197 |

Optimization Problem with Constraints | 215 |

A MultiObjective Evolutionary Algorithm for | 247 |

A Computer Engineering Benchmark | 269 |

Multiobjective Aerodynamic Design | 295 |

Applications of a MultiObjective Genetic | 317 |

MultiObjective Spectroscopic Data Analysis | 341 |

Application of Multiobjective Evolutionary | 365 |

MultiObjective Algorithms for Attribute Selection | 603 |

Financial Applications of MultiObjective | 627 |

Evolutionary MultiObjective Optimization | 653 |

Optimizing Forecast Model Complexity Using | 675 |

Even Flow Scheduling Problems in Forest Management | 701 |

Objective Genetic Algorithm | 716 |

Using Diversity to Guide the Search | 727 |

753 | |

### Other editions - View all

Applications of Multi-Objective Evolutionary Algorithms Carlos A Coello Coello,Gary B Lamont Limited preview - 2004 |

Applications of Multi-objective Evolutionary Algorithms Carlos A. Coello Coello,Gary B. Lamont Limited preview - 2004 |

### Common terms and phrases

analysis application approach approximation average binary chapter chromosome clusters Coello Coello complexity constraints convergence crossover data sets decision variables defined design variables distribution diversity dominated efficient elite solutions encoding Engineering ensemble error evaluations Evolution Strategy Evolutionary Algorithms Evolutionary Computation example feasible Figure fitness genetic algorithm gradients heuristic IEEE implementation individuals initial integer ISPAES iteration knapsack knapsack problem local search machine learning measure Metaheuristics method minimization MMOKP MOEA MOMGA-II multi-objective optimization Multiobjective Evolutionary Algorithms mutation neural networks non-dominated solutions NSGA-II objective function objective space obtained operator optimal solutions optimization problem packet parameters Pareto dominance Pareto front Pareto optimal Pareto optimal set Pareto set partitioning performance population proposed quadratic assignment problem randomly Research screw search space selection shown in Fig simulation single single-objective solutions found solve SPEA strategy subset Table techniques test problems Thiele tion trade-off vector vehicle weights Zitzler