Self-Adaptive Heuristics for Evolutionary Computation

• Κύριος συγγραφέας: Kramer, Oliver (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.
• Σειρά: Studies in Computational Intelligence, 147
• Θέματα: Computer science.; Artificial intelligence.; Computer-aided engineering.; Applied mathematics.; Engineering mathematics.; Computer Science.; Computer-Aided Engineering (CAD, CAE) and Design.; Appl.Mathematics/Computational Methods of Engineering.; Artificial Intelligence (incl. Robotics).

Evolutionary algorithms are successful biologically inspired meta-heuristics. Their success depends on adequate parameter settings. The question arises: how can evolutionary algorithms learn parameters automatically during the optimization? Evolution strategies gave an answer decades ago: self-adapt...

Πίνακας περιεχομένων:

• I: Foundations of Evolutionary Computation
• Evolutionary Algorithms
• Self-Adaptation
• II: Self-Adaptive Operators
• Biased Mutation for Evolution Strategies
• Self-Adaptive Inversion Mutation
• Self-Adaptive Crossover
• III: Constraint Handling
• Constraint Handling Heuristics for Evolution Strategies
• IV: Summary
• Summary and Conclusion
• V: Appendix
• Continuous Benchmark Functions
• Discrete Benchmark Functions.