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The series Studies in Computational Intelligence (SCI) publishes new developments and advances in the various areas of computational intelligence-quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life science, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems and hybrid intelligent systems. Critical to both contributors and readers are the short publication time and world-wide distribution - this permits a rapid and broad dissemination of research results.
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 first 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 Editors 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".
This special low-priced edition is for sale in India, Bangladesh, Bhutan, Maldives, Nepal, Myanmar, Pakistan and Sri Lanka only.
Table of Contents
Introduction Part I : Motivations and Theory Opposition-Based Computing Antithetic and Negatively Associated Random Variables and Function Maximization Opposition and Circularity Part II : Search and Reasoning Collaborative vs. Conflicting Learning, Evolution and Argumentation Proof-Number Search and Its Variants Part 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 Part 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 Part V : Real World Applications Opposite Actions in Reinforced Image Segmentation Opposition Mining in Reservoir Management ISBN - 9788184895629
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Pages : 340
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