LNAI 2903 A Proposal of an Efficient Crossover Using Fitness Prediction and Its Application 1st Edition by Atsuko Mutoh, Tsuyoshi Nakamura, Shohei Kato, Hidenori Itoh ISBN 9783540200574 354020057X

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Product details:
ISBN 10: 354020057X
ISBN 13: 9783540200574
Author: Atsuko Mutoh, Tsuyoshi Nakamura, Shohei Kato, Hidenori Itoh

LNAI 2903 A Proposal of an Efficient Crossover Using Fitness Prediction and Its Application 1st Edition Table of contents:

1. Fundamentals of Evolutionary Algorithms

   • Basic Principles of Genetic Algorithms and Evolutionary Strategies
   • Representation of Individuals in Evolutionary Algorithms
   • Selection, Crossover, and Mutation Operators
   • Convergence and Diversity in Evolutionary Computation

2. Fitness Prediction in Evolutionary Algorithms

   • Introduction to Fitness Evaluation and Its Challenges
   • Methods of Fitness Approximation and Prediction
   • Role of Fitness Prediction in Improving Algorithm Efficiency
   • Techniques for Predicting Fitness in Multi-Objective Optimization

3. Crossover Mechanisms in Evolutionary Algorithms

   • Traditional Crossover Operators: One-Point, Two-Point, and Uniform Crossover
   • Limitations of Standard Crossover Operators
   • Innovations in Crossover Techniques for Enhanced Performance
   • Adaptive and Problem-Specific Crossover Strategies

4. Efficient Crossover Using Fitness Prediction

   • The Concept of Fitness-Aware Crossover
   • Proposals for Fitness Prediction-Based Crossover Operators
   • Algorithms and Models for Efficient Crossover
   • The Impact of Fitness Prediction on Crossover Efficiency

5. Applications of Fitness Prediction-Based Crossover

   • Case Studies in Single-Objective Optimization Problems
   • Applications in Multi-Objective Optimization and Pareto Frontiers
   • Fitness Prediction in Real-World Problems: Engineering Design, Scheduling, and Control Systems
   • Case Example: Evolutionary Optimization in Robotics

6. Empirical Results and Performance Evaluation

   • Benchmark Datasets and Problem Sets Used for Evaluation
   • Comparative Analysis of Fitness Prediction vs. Traditional Crossover
   • Performance Metrics: Convergence Speed, Solution Quality, and Diversity Maintenance
   • Experimental Results and Discussion

7. Advanced Topics in Fitness Prediction and Crossover

   • Hybrid Evolutionary Algorithms: Combining Fitness Prediction with Other Methods
   • Fitness Prediction for Dynamic and Changing Environments
   • Theoretical Analysis of Fitness Prediction-Based Crossover
   • Extending Fitness Prediction to Large-Scale Optimization Problems

8. Challenges and Future Directions

   • Current Challenges in Fitness Prediction and Crossover Efficiency
   • Open Problems in Evolutionary Computation and Fitness Approximation
   • Future Trends in Adaptive Crossover and Fitness Prediction Methods
   • Integration with Machine Learning and Deep Learning Approaches

9. Conclusion

• Summary of Key Contributions and Findings
• The Importance of Fitness Prediction in Evolving Efficient Crossover Methods
• Closing Thoughts on the Future of Evolutionary Algorithms and Optimization

11. Appendices

• Mathematical Models and Proofs for Fitness Prediction

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