Oppositional Concepts in Computational Intelligence

1. Front Matter

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2. Introduction

   1. Introduction

      • H. R. Tizhoosh, Mario Ventresca

      Pages 1-8

3. Part I: Motivations and Theory

   1. Front Matter

      Pages 9-9

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   2. Opposition-Based Computing

      • H. R. Tizhoosh, Mario Ventresca, Shahryar Rahnamayan

      Pages 11-28

   3. Antithetic and Negatively Associated Random Variables and Function Maximization

      • Don L. McLeish

      Pages 29-44

   4. Opposition and Circularity

      • Florencio G. Asenjo

      Pages 45-57

4. Part II: Search and Reasoning

   1. Front Matter

      Pages 59-59

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   2. Collaborative vs. Conflicting Learning, Evolution and Argumentation

      • Luís Moniz Pereira, Alexandre Miguel Pinto

      Pages 61-89

   3. Proof-Number Search and Its Variants

      • H. Jaap van den Herik, Mark H. M. Winands

      Pages 91-118

5. Part III: Optimization

   1. Front Matter

      Pages 119-119

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   2. Improving the Exploration Ability of Ant-Based Algorithms

      • Alice Ralickas Malisia

      Pages 121-142

   3. Differential Evolution Via Exploiting Opposite Populations

      • Shahryar Rahnamayan, H. R. Tizhoosh

      Pages 143-160

   4. Evolving Opposition-Based Pareto Solutions: Multiobjective Optimization Using Competitive Coevolution

      • Tse Guan Tan, Jason Teo

      Pages 161-206

6. Part IV: Learning

   1. Front Matter

      Pages 207-207

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   2. Bayesian Ying-Yang Harmony Learning for Local Factor Analysis: A Comparative Investigation

      • Lei Shi

      Pages 209-232

   3. The Concept of Opposition and Its Use in Q-Learning and Q(λ) Techniques

      • Maryam Shokri, H. R. Tizhoosh, Mohamed S. Kamel

      Pages 233-253

   4. Two Frameworks for Improving Gradient-Based Learning Algorithms

      • Mario Ventresca, H. R. Tizhoosh

      Pages 255-284

7. Part V: Real World Applications

   1. Front Matter

      Pages 285-285

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   2. Opposite Actions in Reinforced Image Segmentation

      • Farhang Sahba, H. R. Tizhoosh

      Pages 287-297

   3. Opposition Mining in Reservoir Management

      • Masoud Mahootchi, H. R. Tizhoosh, Kumaraswamy Ponnambalam

      Pages 299-321

“Theoppositeofacorrectstatementisafalsestatement.Buttheopposite of a profound truth may well be another profound truth.” – Niels Bohr 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 studiedtoanygreatlengthin?eldsoutsideofphilosophyandlogic.This,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 one hand, this volume is a ?st attempt to bring together researchers who are inquiring into the complementary nature of systems and processes and, on the other hand, provide 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 our 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. To this end, we have attempted to provide rough guidelines to understand what makes concepts “oppositional”.

• Opposition-Based Computing
• algorithm
• algorithms
• argumentation
• calculus
• computational intelligence
• differential evolution
• evolution
• image segmentation
• intelligence
• learning
• multi-objective optimization
• optimization
• philosophy
• proving

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