Genetic Algorithms and Grouping Problems

Emanuel Falkenauer shows how to use genetic algorithms to solve several types of problems better than any genetic algorithm technique has done. It is quite gratifying to see such innovation. Lawrence Davis President, TICA Technologies, Inc. Genetic Algorithms and Grouping Problems is truly innovative in presenting new techniques for applying genetic algorithms (GAs). It achieves a greater insight into GA design by placing its use in context, offering the big picture in an easy–to–digest form. This book contains discussions of problems for which genetic algorithms are not well suited, as well as those for which they are. One such problem – and the one that receives most attention in this book – is the area of grouping problems. Genetic Algorithms and Grouping Problems is written for those designing or creating real–world genetic algorithms, whether practitioner or researcher. In addition, the very nature of this author′s discourse will inspire anyone wishing to understand both the benefits and the limitations of GAs.

Spis treści:
HARD PROBLEMS AND GENETIC ALGORITHMS.
What′s the Problem?
Genetic Algorithms.
THE GROUPING GENETIC ALGORITHM.
Grouping Problems.
Drawbacks of Previous GAs for Grouping Problems.
The Grouping Genetic Algorithm.
INDUSTRIAL APPLICATIONS OF GROUPING GENETIC ALGORITHMS.
Bin Packing and Line Balancing.
Economies of Scale.
Creation of Part Families–Conceptual Clustering.
Equal Piles.
EPILOGUE.
Where Have We Been?
Where Are We Now?
Where Are We Heading?
Appendices.
The NP–Hard Problems.
Complexity and Genetic Research.
References.
Index.

Okładka tylna:
Emanuel Falkenauer shows how to use genetic algorithms to solve several types of problems better than any genetic algorithm technique has done. It is quite gratifying to see such innovation. Lawrence Davis President, TICA Technologies, Inc. Gene tic Algorithms and Grouping Problems is truly innovative in presenting new techniques for applying genetic algorithms (GAs). It achieves a greater insight into GA design by placing its use in context, offering the big picture in an easy–to–digest form. This book contains discussions of problems for which genetic algorithms are not well suited, as well as those for which they are. One such problem – and the one that receives most attention in this book – is the area of grouping problems. Genetic Algorithms and Grouping Problems is written for those designing or creating real–world genetic algorithms, whether practitioner or researcher. In addition, the very nature of this author′s discourse will inspire anyone wishing to understand both the benefits and the limitations of GAs.