The mystique of biologically inspired (or bioinspired) paradigms is their ability to describe and solve complex relationships from intrinsically very simple initial conditions and with little or no knowledge of the search space. Edited by two prominent, well-respected researchers, the Handbook of Bioinspired Algorithms and Applications reveals the connections between bioinspired techniques and the development of solutions to problems that arise in diverse problem domains.
A repository of the theory and fundamentals as well as a manual for practical implementation, this authoritative handbook provides broad coverage in a single source along with numerous references to the available literature for more in-depth information. The book's two sections serve to balance coverage of theory and practical applications. The first section explains the fundamentals of techniques, such as evolutionary algorithms, swarm intelligence, cellular automata, and others. Detailed examples and case studies in the second section illustrate how to apply the theory in actually developing solutions to a particular problem based on a bioinspired technique.
Emphasizing the importance of understanding and harnessing the robust capabilities of bioinspired techniques for solving computationally intractable optimizations and decision-making applications, the Handbook of Bioinspired Algorithms and Applications is an absolute must-read for anyone who is serious about advancing the next generation of computing.
Table of Contents
MODELS AND PARADIGMS. Evolutionary Algorithms. An Overview of Neural Networks Models. Ant Colony Optimization. Swarm Intelligence. Parallel Genetic Programming: Methodology, History, and Application to Real Life Problems. Parallel Cellular Algorithms and Programs. Decentralized Cellular Evolutionary Algorithms. Optimization Via Gene Expression Algorithms. Dynamic Updating DNA Computing Algorithms. A Unified View on Metaheuristics and Their Hybridization. The Foundations of Autonomic Computing. APPLICATION DOMAINS. Setting Parameter Values for Parallel Genetic Algorithms: Scheduling Tasks on a Cluster. Genetic Algorithms for Scheduling in Grid Computing Environments: A Case Study. Minimization of SADMs in Unidirectional SONET/WDM Rings Using Genetic Algorithm. Solving Optimization Problems in Wireless Networks Using Genetic Algorithms. Medical Imaging and Diagnosis Using Genetic Algorithms. Multiprocessor Scheduling and Rescheduling with Use of Cellular Automata. Cellular Automata, PDEs, and Pattern Formation. Ant Colonies and the Mesh Partitioning Problem. Simulating the Strategic Adaptation of Organizations Using OrgSwarm. BeeHive: New Ideas for Developing Routing Algorithms Inspired by Honey Bee Behavior. Swarming Agents for Decentralized Clustering in Spatial Data. Biological Inspired Based Intrusion Detection Models for Mobile Telecommunication Systems. Synthesis of Multiple-Valued Circuits by Neural Networks. On the Computing Capacity of Multiple-Valued Multiple Threshold Perceptrons. Advanced Evolutionary Algorithms for Training Neural Networks. Bio-Inspired Data Mining. A Hybrid Evolutionary Algorithm for Knowledge Discovery in Microarray Experiments. Evolutionary Approach to Electrical Engineering Design Problems. Solving the Partitioning Problem in Distributed Virtual Environment Systems Using Evolutive Algorithms. Population Learning Algorithm and Its Applications. Biology-Derived Algorithm in Engineering Optimization. Biomimetic Models for Wireless Sensor
Stephan Olariu, Albert Y. Zomaya