Oppositional concepts in computational intelligence

This volume is motivated in part by the observation that opposites permeate everything around us, in some form or another. Its study has attracted the attention of countless minds for at least 2500 years. However, due to the lack of an accepted mathematical formalism for opposition it has not been e...

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Kaydedildi:
Detaylı Bibliyografya
Yazar: Tizhoosh, Hamid R.
Diğer Yazarlar: Ventresca, M. (Editör), Kacprzyk, Janusz, 1947- (Yayın yönetmeni), Tizhoosh, Hamid R., 19..- (Yayın yönetmeni), Ventresca, Mario, 19..- (Yayın yönetmeni)
Materyal Türü: Livre numérique
Dil:Anglais
Baskı/Yayın Bilgisi: Berlin, Heidelberg : Springer Berlin Heidelberg [20..].
Cham : Springer Nature
Edisyon:1st ed. 2008.
Seri Bilgileri:Studies in Computational Intelligence 155
Online Erişim:Accès sur la plateforme de l'éditeur
Accès sur la plateforme Istex
Accès Université d'Orléans
Accès INSA CVL
Not: Archives Springer e-books (Licence nationale)
Archives Springer e-books (Licence nationale)
Autres localisations: Voir dans le Sudoc
Edition sous un autre format:• Oppositional Concepts in Computational Intelligence, Texte imprimé, 9783540867036
• Oppositional Concepts in Computational Intelligence, Texte imprimé, 9783642089787
• Oppositional Concepts in Computational Intelligence, Texte imprimé, 9783540708261
• Oppositional Concepts in Computational Intelligence, Texte imprimé, 9783540867036
• Oppositional Concepts in Computational Intelligence, Texte imprimé, 9783642089787
• Oppositional Concepts in Computational Intelligence, Texte imprimé, 9783540708261
İçindekiler:
  • I: Motivations and Theory Opposition-Based Computing Antithetic and Negatively Associated Random Variables and Function Maximization Opposition and Circularity II: Search and Reasoning Collaborative vs. Conflicting Learning, Evolution and Argumentation Proof-Number Search and Its Variants III: Optimization Improving the Exploration Ability of Ant-Based Algorithms Differential Evolution Via Exploiting Opposite Populations Evolving Opposition-Based Pareto Solutions: Multiobjective Optimization Using Competitive Coevolution IV: Learning Bayesian Ying-Yang Harmony Learning for Local Factor Analysis: A Comparative Investigation The Concept of Opposition and Its Use in Q-Learning and Q(?) Techniques Two Frameworks for Improving Gradient-Based Learning Algorithms V: Real World Applications Opposite Actions in Reinforced Image Segmentation Opposition Mining in Reservoir Management.