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3 edition of Multi-agent applications with evolutionary computation and biologically inspired technologies found in the catalog.

Multi-agent applications with evolutionary computation and biologically inspired technologies

Yasushi Kambayashi

Multi-agent applications with evolutionary computation and biologically inspired technologies

intelligent techniques for ubiquity and optimization

by Yasushi Kambayashi

  • 301 Want to read
  • 5 Currently reading

Published by Medical Information Science Reference in Hershey, PA .
Written in English


Edition Notes

Includes bibliographical references and index.

StatementYasushi Kambayashi, editor
Classifications
LC ClassificationsQA76.76.I58 M78 2010
The Physical Object
Paginationp. cm.
ID Numbers
Open LibraryOL24493709M
ISBN 109781605668987, 9781605668994
LC Control Number2010011642

The area of biologically inspired computing, or biological computation, involves the development of new, biologically based techniques for solving difficult computational problems.A unified overview of computer science ideas inspired by biology, Biological Computation presents the most fundamental and significant concepts in this area. In the book, students discover that bacteria communicate Cited by: 8. Description COSC , , and focus on biologically-inspired computation, including recent developments in computational methods inspired by nature, such as neural networks, genetic algorithms and other evolutionary computation systems, ant swarm optimization, artificial immune systems, swarm intelligence, cellular automata, and multi-agent systems.

Evolutionary algorithms is one of the subfields of artificial intelligence, and is an effective algorithm for global optimization inspired by biological evolution. With the rapidly growing complexity of design issues, methodologies and more demanding quality of health technology applications, the development of evolutionary computation. CS focuses on biologically-inspired computation, including recent developments in computational methods inspired by nature, such as neural networks, genetic algorithms and other evolutionary computation systems, ant swarm optimization, artificial immune systems, swarm intelligence, cellular automata, and multi-agent systems.

  Evolution has provided a source of inspiration for algorithm designers since the birth of computers. The resulting field, evolutionary computation, has been Cited by:   Evolutionary computation will drive the future of creative AI based on algorithms inspired by biological evolution. Through reproduction, mutation, recombination, and selection, evolutionary.


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Multi-agent applications with evolutionary computation and biologically inspired technologies by Yasushi Kambayashi Download PDF EPUB FB2

Multi-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies: Intelligent Techniques for Ubiquity and Optimization compiles numerous ongoing projects and research efforts in the design of agents in light of recent development in neurocognitive science and quantum physics.

This innovative collection provides readers with interdisciplinary applications of multi-agents systems. Multi-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies: Intelligent Techniques for Ubiquity and Optimization compiles numerous ongoing projects and research efforts in the design of agents in light of recent development in neurocognitive science and quantum : IGI Global.

Multi-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies: Intelligent Techniques for Ubiquity and Optimization (, Hardcover) Be the first to write a review. About this product. Stock photo. Stock photo. Brand new: lowest price. The lowest-priced brand-new, unused, unopened, undamaged item in its original packaging (where packaging is applicable).

Multi-agent applications with evolutionary computation and biologically inspired technologies: intelligent techniques for ubiquity and optimization. [Shu-Heng Chen; Yasushi Kambayashi; Hiroshi Sato;] -- "This book compiles numerous ongoing projects and research efforts in the design of agents in light of recent development in neurocognitive science and quantum physics, providing readers with.

This book addresses agent-based computing, concentrating in particular on evolutionary multi-agent systems (EMAS), which have been developed since at the AGH University of Science and Technology in Cracow, Poland.

It provides the relevant background information on and a. The aim of this paper is to give a survey on the development and applications of evolutionary multi-agent systems (EMAS).

The paper starts with a general introduction describing the background, structure and behaviour of by: "A Multi-Agent System Forecast of the S&P/Case-Shiller LA Home Price Index." In Multi-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies: Intelligent Techniques for Ubiquity and Optimization.

edited by Shu-Heng Chen, Yasushi Kambayashi, and Hiroshi Sato, Cited by: 1. Evolutionary Multi-Agent Systems: An Adaptive and Dynamic Approach to Optimization This paper explores the ability of a virtual team of specialized strategic software agents to cooperate and evolve to adaptively search an optimization design space.

Our goal is to demonstrate and understand how such dynamically evolving teams may. The aim of this paper is to give a survey on the development and applications of evolutionary multi-agent systems (EMAS).

The paper starts with a general introduction describing the background. The two volume set LNCS / constitutes the refereed proceedings of the Genetic and Evolutionary Computation Conference, GECCOheld in Seattle, WA, USA, in June The revised full papers and poster papers presented were.

The two volumes LNCS and constitute the refereed conference proceedings of the 19th European Conference on the Applications of Evolutionary Computation, EvoApplicationsheld in Porto, Portugal, in March/Aprilco-located with the. A comprehensive introduction to new approaches in artificial intelligence and robotics that are inspired by self-organizing biological processes and structures.

New approaches to artificial intelligence spring from the idea that intelligence emerges as much from cells, bodies, and societies as it does from evolution, development, and learning.

Swarm Intelligence and bio-inspired computation have become increasing popular in the last two decades. Bio-inspired algorithms such as ant colony algorithms, bat algorithms, bee algorithms, firefly algorithms, cuckoo search and particle swarm optimization have been applied in almost every area of science and engineering with a dramatic increase of number of relevant publications.

Recent Developments in Biologically Inspired Computing is necessary reading for undergraduate and graduate students, and researchers interested in knowing the most recent advances in problem solving techniques inspired by nature. This book covers the most relevant areas in computational intelligence, including evolutionary algorithms, artificial neural networks, artificial immune systems and.

Applications of Evolutionary Computation: 19th European Conference, EvoApplicationsPorto, Portugal, March 30 -- April 1,Proceedings, Part I pp Christophe Atten.

Readers will also find papers describing applications in such areas as data mining, adaptive control, medical and bioinformatics, games and multi-media, agent-based computing and modeling, complex systems, and chemical and biological systems.

Captures and archives advances in the field of evolutionary intelligence. Introduction. The development of human society is inseparable from the study of complex systems. The complex system attract much interest because of its universal or special state and structure, such as the planets in the universe that attract or reject each other, the political or strategic relationships between nations, the social circles in which individuals live, and the proteins that Cited by: 4.

Alers S, Claes D, Tuyls K and Weiss G Biologically inspired multi-robot foraging Proceedings of the international conference on Autonomous agents and multi-agent systems, () Li W, Gauci M and Gross R Coevolutionary learning of swarm behaviors without metrics Proceedings of the Annual Conference on Genetic and Evolutionary.

Introducing a handbook for gene regulatory network research using evolutionary computation, with applications for computer scientists, computational and system biologists. This book is a step-by-step guideline for research in gene regulatory networks (GRN) using evolutionary computation (EC).

Evolutionary computation can be of considerable use in interpreting and analyzing spectra of biological systems. This chapter focuses on the electron paramagnetic resonance (EPR) technology, and on the use of an evolutionary computational approach to aid the characterization of biological systems with EPR.

An artificial immune system based multi-agent model and its application to robot cooperation problem Abstract: Artificial immune system (AIS) imitates the natural immune system that has sophisticated methodologies and capabilities to build computational algorithms that solves engineering problems by: The volume relates Fogel's approach, called evolutionary programming, to biological evolution, alternative applications of evolution to software, and the general quest for artificial intelligence.

It offers a carefully organized, integrated exposition of a large body of research originally published elsewhere.The AGILE Design of Reality Game AI, book chapter in Multi-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies: Intelligent Techniques for Ubiquity and Optimization, – Che, X., Ali, M.

and Reynolds, R. G. (). Robust Evolution Optimization at the Edge of Chaos: Commercialization of Culture.