Series in Intelligent Control and Intelligent Automation - Vol. 13
NETWORK-BASED DISTRIBUTED PLANNING USING COEVOLUTIONARY ALGORITHMS
by Raj Subbu (General Electric Global Research, USA) & Arthur C Sanderson (Rensselaer Polytechnic Institute, USA)
In this book, efficient and scalable coevolutionary algorithms for distributed, network-based decision-making, which utilize objective functions are developed in a networked environment where internode communications are a primary factor in system performance.
A theoretical foundation for this class of coevolutionary algorithms is introduced using techniques from stochastic process theory and mathematical analysis.
A case study in distributed, network-based decision-making presents an implementation and detailed evaluation of the coevolutionary decision-making framework that incorporates distributed evolutionary agents and mobile agents.
The methodology discussed in this book can have a fundamental impact on the principles and practice of engineering in the distributed, network-based environment that is emerging within and among corporate enterprise systems. In addition, the conceptual framework of the approach to distributed decision systems described may have much wider implications for network-based systems and applications.
Contents:
- Background and Related Work
- Problem Formulation and
Analysis
- Theory and Analysis of Evolutionary Optimization
- Theory and Analysis of Distributed Coevolutionary Optimization
- Performance Evaluation Based on Ideal Objectives
- Coevolutionary Virtual Design Environment
- Evaluation and Analysis
Readership: Researchers and engineers in artificial intelligence,
evolutionary computation and decision sciences.
| 192pp |
Pub. date: Feb 2004 |
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