Oppositional Concepts in Computational Intelligence

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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 explicitly studied to any great length in fields outside of philosophy and logic. Despite the fact that we observe opposition everywhere in nature, our minds seem to divide the world into entities and opposite entities; indeed we use opposition everyday. We have become so accustomed to opposition that its existence is accepted, not usually questioned and its importance is constantly overlooked.

On the one hand, this volume is a fist attempt to bring together researchers who are inquiring into the complementary nature of systems and processes and, on the other hand, it provides some elementary components for a framework to establish a formalism for opposition-based computing. From a computational intelligence perspective, many successful opposition-based concepts have been in existence for a long time. It is not the authors intention to recast these existing methods, rather to elucidate that, while diverse, they all share the commonality of opposition - in one form or another, either implicitly or explicitly. Therefore they have attempted to provide rough guidelines to understand what makes concepts "oppositional".

Author(s): H. R. Tizhoosh, Mario Ventresca (auth.), Hamid R. Tizhoosh, Mario Ventresca (eds.)
Series: Studies in Computational Intelligence 155
Edition: 1
Publisher: Springer-Verlag Berlin Heidelberg
Year: 2008

Language: English
Pages: 328
Tags: Appl.Mathematics/Computational Methods of Engineering; Artificial Intelligence (incl. Robotics)

Front Matter....Pages -
Introduction....Pages 1-8
Front Matter....Pages 9-9
Opposition-Based Computing....Pages 11-28
Antithetic and Negatively Associated Random Variables and Function Maximization....Pages 29-44
Opposition and Circularity....Pages 45-57
Front Matter....Pages 59-59
Collaborative vs. Conflicting Learning, Evolution and Argumentation....Pages 61-89
Proof-Number Search and Its Variants....Pages 91-118
Front Matter....Pages 119-119
Improving the Exploration Ability of Ant-Based Algorithms....Pages 121-142
Differential Evolution Via Exploiting Opposite Populations....Pages 143-160
Evolving Opposition-Based Pareto Solutions: Multiobjective Optimization Using Competitive Coevolution....Pages 161-206
Front Matter....Pages 207-207
Bayesian Ying-Yang Harmony Learning for Local Factor Analysis: A Comparative Investigation....Pages 209-232
The Concept of Opposition and Its Use in Q -Learning and Q ( λ ) Techniques....Pages 233-253
Two Frameworks for Improving Gradient-Based Learning Algorithms....Pages 255-284
Front Matter....Pages 285-285
Opposite Actions in Reinforced Image Segmentation....Pages 287-297
Opposition Mining in Reservoir Management....Pages 299-321
Back Matter....Pages -