Diccionario politécnico Beigbeder
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Written for lecturers, graduate and undergraduate students, this textbook contains a thorough introduction to evolutionary computing (EC), including the basics of all traditional variants (evolution strategies, evolutionary programming, genetic algorithms, and genetic programming, learning classifier systems); EC themes of general interest (such as algorithm parameter control, constraint handling, and hybridisation); a collection of particular EC techniques (e.g., niching, co-evolution, multi-objective EAs). Particularly interesting features for this group are the exercises after chapters, corresponding slides online, a lecturer's guide with answers/solutions to exercises and exam questions with answers. This book is also meant for those who wish to apply evolutionary computing to a particular problem or within a given application area. To this group the book is valuable because it presents EC as something to be used, rather than just being studied, and it contains an explicit treatment of evolutionary algorithm design and guidelines for good experimentation. Last, but not least, this book contains information on the current state of the art in a wide range of subjects, an extensive literature list and pointers to online information sources which may be interesting to fellow researchers as a quick reference on subjects outside of their own specialism within evolutionary computing.
INDICE: Introduction.- What is an Evolutionary Algorithm?- Genetic Algorithms.- Evolution Strategies.- Evolutionary Programming.- Genetic Programming.- Learning Classifier Systems.- Parameter Control in Evolutionary Algorithms.- Multi-Modal Problems and Spatial Distribution.- Related Techniques and Hybridisation.- Theory.- Constraint Handling.- Special Forms of Evolution.- Working with Evolutionary Algorithms.- Summary.- Appendix A. Gray Coding.- Appendix B. Test Functions.- Index.- References.
Lecturers, graduate and untergraduate students
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