Framework for Workflow Gridication of Genetic Algorithms in Java 1st edtion by Boro Jakimovski, Darko Cerepnalkoski, Goran Velinov ISBN 3540693888 9783540693888

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Product details:

ISBN 10:       3540693888
ISBN 13: 978-3540693888
Author:         Boro Jakimovski, Darko Cerepnalkoski, Goran Velinov

In this paper we present new Java framework for Gridification of Genetic Algorithms. The framework enables easy implementation of Genetic Algorithms and also enables researchers easy and stable usage of the Grid for their deployment. The design of the framework was based on principles that make it very open and extensible. The Grid components use pure Java implementation of Grid job submission and retrieval for the Glite grid middleware by using Web Services (WS). The framework was tested on the SEEGRID testbed. Using this framework we have developed a pilot application for optimizing data warehousing VIS problem.

Framework for Workflow Gridication of Genetic Algorithms in Java 1st Table of contents:

Chapter 1: Introduction
1.1 Overview of Genetic Algorithms
1.2 Need for Grid Computing in Genetic Algorithms
1.3 The Concept of Workflow Gridication
1.4 Motivation for Gridifying Genetic Algorithms
1.5 Objectives and Scope of the Framework
1.6 Structure of the Paper

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Chapter 2: Background and Related Work
2.1 Introduction to Genetic Algorithms (GA)
2.2 Key Components of Genetic Algorithms
2.3 Grid Computing: Concepts and Technologies
2.4 Workflow Management in Grid Computing
2.5 Previous Work on Gridification of Evolutionary Algorithms
2.6 Challenges in Gridification of GAs

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Chapter 3: Genetic Algorithms and Their Applications
3.1 Basic Principles of Genetic Algorithms
3.2 Types of Genetic Algorithms and Variants
3.3 Applications of Genetic Algorithms in Real-World Problems
3.4 Advantages and Limitations of Genetic Algorithms
3.5 Why Grid Computing Enhances Genetic Algorithms

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Chapter 4: Introduction to Grid Computing and Workflow Management
4.1 What is Grid Computing?
4.2 Key Characteristics of Grid Computing
4.3 Architecture of Grid Systems
4.4 Workflow Management in Grid Systems
4.5 Gridification Challenges and Solutions in Workflow Systems

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Chapter 5: The Concept of Workflow Gridication
5.1 Defining Workflow Gridication
5.2 Key Principles in Workflow Gridication
5.3 How Gridification Improves Performance and Scalability
5.4 Workflow Execution Models in Grid Computing
5.5 Gridification of Evolutionary Algorithms: Opportunities and Challenges

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Chapter 6: Framework Design and Architecture
6.1 Overview of the Proposed Framework
6.2 System Architecture for Gridifying Genetic Algorithms
6.3 Designing the Workflow Components
6.4 Modular Structure of the Framework
6.5 Integration with Java and Grid Platforms
6.6 Scalability and Flexibility Considerations

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Chapter 7: Implementation of the Framework
7.1 Tools and Technologies Used
7.2 Java Implementation Details
7.3 Workflow Creation and Management
7.4 Grid Resource Allocation and Scheduling
7.5 Fault Tolerance and Error Handling in the Framework
7.6 Performance Optimizations in the Implementation

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Chapter 8: Gridification of Genetic Algorithms Using the Framework
8.1 Steps to Gridify Genetic Algorithms
8.2 Adapting Genetic Operators for Grid Computing
8.3 Parallelization of GA Operations on Grid Systems
8.4 Case Study: Gridified GA for Optimization Problems
8.5 Experimental Evaluation of Gridified GAs

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Chapter 9: Performance Evaluation and Results
9.1 Evaluation Criteria for the Framework
9.2 Benchmarking Genetic Algorithm Performance
9.3 Comparative Analysis of Gridified vs. Non-Gridified GAs
9.4 Scalability and Efficiency Testing
9.5 Impact of Grid Resources on GA Performance

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Chapter 10: Applications and Use Cases
10.1 Solving Optimization Problems with Gridified GAs
10.2 Applications in Scientific Simulations
10.3 Gridified GAs in Machine Learning and Data Mining
10.4 Use of Gridified GAs in Bioinformatics
10.5 Other Potential Use Cases of the Framework

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Chapter 11: Challenges and Future Work
11.1 Challenges in Gridifying Genetic Algorithms
11.2 Grid Resource Management Issues
11.3 Overcoming Bottlenecks in Workflow Execution
11.4 Future Enhancements to the Framework
11.5 Integration with Advanced Grid and Cloud Computing Systems

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Chapter 12: Conclusion
12.1 Summary of Contributions
12.2 Impact of Workflow Gridification on Genetic Algorithms
12.3 Key Findings and Observations
12.4 Final Thoughts on the Future of Gridified Genetic Algorithms

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