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Introduction

Evolutionary algorithms have gained popularity in recent years for their ability to optimize complex problems by mimicking natural selection and evolution processes. One particularly interesting application of evolutionary algorithms is in portfolio optimization, where the goal is to maximize returns while minimizing risk. This thesis aims to explore how evolutionary algorithms can be employed in portfolio optimization to create more efficient and reliable investment strategies.

1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter Two: Literature Review
2.1 Overview of Portfolio Optimization
2.2 Traditional Optimization Methods
2.3 Evolutionary Algorithms in Optimization
2.4 Evolutionary Algorithms in Portfolio Optimization
2.5 Comparison of Evolutionary Algorithms with Traditional Methods
2.6 Applications of Evolutionary Algorithms in Financial Markets
2.7 Challenges and Limitations of Evolutionary Algorithms in Portfolio Optimization
2.8 Current Research Trends in Evolutionary Algorithms and Portfolio Optimization
2.9 Gaps in the Literature
2.10 Conclusion

Chapter Three: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Variable Selection
3.4 Evolutionary Algorithm Selection
3.5 Portfolio Optimization Model
3.6 Performance Metrics
3.7 Validation Techniques
3.8 Ethical Considerations

Chapter Four: Discussion of Findings
4.1 Analysis of Results
4.2 Comparison of Evolutionary Algorithms in Portfolio Optimization
4.3 Impact of Parameters on Performance
4.4 Insights into Investment Strategies
4.5 Robustness of Evolutionary Algorithms in Dynamic Markets
4.6 Practical Implications
4.7 Future Research Directions
4.8 Conclusion

Chapter Five: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to Knowledge
5.3 Practical Implications
5.4 Recommendations for Practitioners
5.5 Limitations of the Study
5.6 Suggestions for Future Research
5.7 Conclusion

Thesis Overview on Evolutionary Algorithms in Portfolio Optimization

Evolutionary algorithms have shown great potential in optimizing complex problems such as portfolio optimization. This thesis aims to investigate the application of evolutionary algorithms in creating efficient and reliable investment strategies. The introduction provides a background of the study, problem statement, objectives, limitations, scope, significance, structure, and definition of terms.

The literature review explores the traditional methods of portfolio optimization, the use of evolutionary algorithms in optimization, comparison with traditional methods, challenges, and current research trends in the field. The methodology chapter outlines the research design, data collection, variable selection, algorithm selection, model, metrics, and validation techniques.

The discussion of findings chapter analyzes results, compares algorithms, assesses parameter impact, provides insights into strategies, explores robustness in dynamic markets, implications, and future directions. The conclusion chapter summarizes findings, contributions, implications, limitations, future research, and concludes the thesis on evolutionary algorithms in portfolio optimization.

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