Gene Expression Programming 1st edition by Candida Ferreira ISBN 3540821384  978-3540821380

Gene Expression Programming 1st edition by Candida Ferreira – Ebook PDF Instant Download/Delivery. 3540821384  978-3540821380
Full download EGene Expression Programming 1st edition after payment

Product details:

ISBN 10: 3540821384
ISBN 13: 978-3540821380
Author: Candida Ferreira 

This book describes the basic ideas of gene expression programming (GEP) and numerous modifications to this powerful new algorithm. It provides all the implementation details of GEP so that anyone with elementary programming skills will be able to implement it themselves. The book includes a self-contained introduction to this new exciting field of computational intelligence. This second edition has been revised and extended with five new chapters.

Gene Expression Programming 1st Table of contents:

Chapter 1: Introduction to Gene Expression Programming
1.1 Overview of Evolutionary Computation
1.2 Gene Expression Programming: An Introduction
1.3 Historical Background and Development
1.4 Core Principles of Gene Expression Programming
1.5 GEP vs. Other Evolutionary Algorithms (Genetic Algorithms, GP, etc.)
1.6 Scope and Structure of the Book

Chapter 2: Fundamental Concepts of GEP
2.1 Genes, Chromosomes, and Genomes
2.2 Gene Expression and Phenotype Generation
2.3 The GEP Algorithm: Step-by-Step Process
2.4 Operators in GEP: Mutation, Crossover, and Inversion
2.5 Encoding and Representation in GEP
2.6 Evolutionary Mechanisms in GEP

Chapter 3: Genetic Operators in GEP
3.1 Crossover and Its Variants
3.2 Mutation and Inversion Techniques
3.3 Selection Strategies
3.4 Reproduction and Elitism
3.5 Gene Expression Control Mechanisms
3.6 Fine-tuning GEP Parameters for Better Results

Chapter 4: GEP Representation and Encoding Schemes
4.1 The Role of Linear and Tree-Based Representation
4.2 Function Set and Terminal Set Design
4.3 Gene and Chromosome Structure in GEP
4.4 Expression Trees vs. Expression Graphs
4.5 Adaptations of GEP for Different Problems
4.6 Advantages and Limitations of GEP Representation

Chapter 5: GEP Operators and Algorithmic Design
5.1 Designing the Fitness Function for GEP
5.2 Exploration vs. Exploitation in GEP
5.3 Multi-objective Optimization with GEP
5.4 Convergence Behavior and Stopping Criteria
5.5 Advanced GEP Operators: Simplification, Repair, and Local Search
5.6 Parallel and Distributed GEP

Chapter 6: Applications of Gene Expression Programming
6.1 GEP in Symbolic Regression
6.2 Function Discovery and Mathematical Modeling
6.3 GEP in Classification and Prediction Problems
6.4 GEP in Optimization: Combinatorial and Continuous Problems
6.5 GEP for Data Mining and Feature Selection
6.6 Real-World Applications in Engineering, Biology, and Finance

Chapter 7: Advanced Topics in Gene Expression Programming
7.1 Genetic Diversity in GEP
7.2 Hybrid Approaches: GEP + Other Evolutionary Algorithms
7.3 Multimodal and Multi-objective GEP
7.4 Self-adaptive Gene Expression Programming
7.5 GEP in Artificial Neural Networks and Hybrid Systems
7.6 Theoretical Insights: Complexity, Generalization, and Overfitting

Chapter 8: Practical Aspects of GEP Implementation
8.1 Implementing GEP in Programming Languages (e.g., C++, Python, Java)
8.2 Building a GEP Framework for Custom Problems
8.3 Debugging and Testing GEP Models
8.4 Performance Evaluation and Benchmarking
8.5 Optimizing GEP Algorithmic Efficiency
8.6 Software Tools and Libraries for GEP

Chapter 9: Comparative Analysis of GEP with Other Evolutionary Algorithms
9.1 GEP vs. Genetic Algorithms (GA)
9.2 GEP vs. Genetic Programming (GP)
9.3 GEP vs. Differential Evolution (DE)
9.4 GEP vs. Particle Swarm Optimization (PSO)
9.5 Performance Metrics and Case Studies
9.6 Strengths and Weaknesses of GEP in Various Applications

Chapter 10: Future Trends in Gene Expression Programming
10.1 Emerging Trends and Innovations in GEP
10.2 The Role of Artificial Intelligence and Deep Learning
10.3 GEP in Real-Time and Big Data Applications
10.4 The Future of Evolutionary Algorithms in Computational Intelligence
10.5 Interdisciplinary Applications and Interactions
10.6 Ethical Considerations and Societal Impact

Chapter 11: Case Studies and Real-World Implementations
11.1 Case Study 1: GEP for Financial Forecasting
11.2 Case Study 2: GEP in Bioinformatics and Drug Design
11.3 Case Study 3: GEP in Robotics and Control Systems
11.4 Case Study 4: GEP in Environmental and Ecological Modeling
11.5 Case Study 5: GEP in Software Engineering and Code Optimization
11.6 Lessons Learned from GEP Applications

Chapter 12: Conclusion
12.1 Summary of Key Concepts and Techniques in GEP
12.2 Challenges and Opportunities in Gene Expression Programming
12.3 The Evolving Role of GEP in Computational Intelligence
12.4 Final Thoughts and Future Directions for GEP Research

References
Index

People also search for Gene Expression Programming 1st:

self learning gene expression programming

gene expression programming python

gene expression programming (gep)

gene expression programming software

gene expression programming matlab