LNAI 2801 Distributed Genetic Algorithm Learning by Direct Exchange of Chromosomes 1st Edition by Aleš Kubík ISBN 9783540200574 354020057X

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Product details:
ISBN 10: 354020057X
ISBN 13: 9783540200574
Author: Aleš Kubík

Genetic algorithms is a technique widely used to evolve controllers of agents or robots in dynamic environments. In this paper we describe a modification to a single-robot-based evolution of a controller – a distributed parallel genetic algorithm where the pool of chromosomes is dispersed over a multi-robot society. Robots share their experience in solving the task by direct exchange of individually evolved successful strategies coded by chromosomes.

LNAI 2801 Distributed Genetic Algorithm Learning by Direct Exchange of Chromosomes 1st Edition Table of contents:

Chapter 1: Introduction to Genetic Algorithms

• Basic Concepts of Genetic Algorithms (GAs)
• History and Evolution of Genetic Algorithms
• Key Components of a Genetic Algorithm: Population, Selection, Crossover, and Mutation
• Applications of Genetic Algorithms in Optimization and Learning

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Chapter 2: The Need for Distributed Genetic Algorithms

• Challenges in Standard Genetic Algorithms
• Limitations of Centralized Genetic Algorithms
• The Benefits of Parallelization and Distribution in GAs
• Overview of Distributed Genetic Algorithms (DGAs)

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Chapter 3: Principles of Distributed Genetic Algorithms

• The Structure of Distributed Genetic Algorithms
• Population Subdivision and Distribution Strategies
• Communication Mechanisms Between Subpopulations
• Load Balancing and Resource Allocation in Distributed Systems

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Chapter 4: Direct Exchange of Chromosomes in Distributed Genetic Algorithms

• The Concept of Chromosome Exchange
• Mechanisms for Direct Chromosome Exchange
• Benefits of Direct Exchange Over Other Approaches (e.g., Migration)
• The Impact on Convergence and Diversity in the Population

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Chapter 5: Implementation of the Distributed Genetic Algorithm

• Designing the Distributed System for Genetic Algorithms
• The Role of the Master-Slave Architecture in DGA
• Implementing Chromosome Exchange in Distributed Environments
• Handling Communication and Synchronization Across Nodes

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Chapter 6: Performance Metrics and Evaluation of Distributed Genetic Algorithms

• Key Performance Indicators for Genetic Algorithms
• Measuring Convergence Speed and Solution Quality
• Comparison Between Centralized and Distributed Genetic Algorithms
• Evaluating the Effectiveness of Chromosome Exchange Strategies

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Chapter 7: Applications of Distributed Genetic Algorithms

• Real-World Applications of Distributed GAs
• Use of Distributed GAs in Optimization Problems
• Case Studies: Solving Complex Engineering and Scientific Problems
• Role of Chromosome Exchange in Practical Applications

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Chapter 8: Advanced Techniques and Improvements

• Adaptive Strategies in Distributed Genetic Algorithms
• Incorporating Hybridization with Other Evolutionary Techniques
• Self-Organizing and Dynamic Population Models
• Enhancing Diversity and Avoiding Premature Convergence

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Chapter 9: Experimental Results and Case Studies

• Experimental Setup and Testing of Distributed Genetic Algorithms
• Performance Results from Different Problem Domains
• Analysis of the Effectiveness of Direct Chromosome Exchange
• Case Studies and Success Stories in Various Industries

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Chapter 10: Future Directions in Distributed Genetic Algorithms

• The Future of Genetic Algorithms in Distributed Systems
• Exploring the Use of Artificial Intelligence and Machine Learning in Genetic Algorithms
• Scaling Distributed GAs to Handle Large-Scale Problems
• The Potential for Real-Time and Adaptive Distributed Systems

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