The Settings tab within the Portfolio Builder section of the Result Analysis module allows users to configure various parameters for optimizing their portfolio. These settings define how the Portfolio Builder will combine and evaluate different reports to construct the best possible portfolio based on user-defined criteria.

Optimization Type

Simple Optimization

Description: Simple Optimization is a brute force optimization method where the builder systematically evaluates all possible parameter combinations. This exhaustive search ensures that every potential portfolio configuration is considered to find the optimal solution. However, it can be time-consuming and computationally intensive, especially with a large number of parameters.

Use Case: Simple Optimization is suitable for scenarios with a relatively small set of parameters where a thorough examination of all possibilities is feasible and desirable.

Note: If Simple Optimization is selected, Genetic Options are unavailable.

Genetic Optimization

Description: Genetic Optimization uses genetic algorithms to find the optimal portfolio configuration. Genetic algorithms are inspired by the principles of natural selection and evolution, where the fittest individuals are selected for reproduction to produce the next generation. This method is more efficient than brute force as it focuses on exploring the most promising areas of the parameter space.

Process:

• Initialization: A population of potential solutions (portfolios) is randomly generated.
  
  
• Selection: The best-performing portfolios are selected based on a fitness function.
  
  
• Crossover: Selected portfolios exchange their "genes" (parameters) to create new portfolios.
  
  
• Mutation: Random changes are introduced to some portfolios to maintain diversity and explore new areas.
  
  
• Evaluation: The new generation of portfolios is evaluated, and the process repeats for a specified number of generations or until convergence.

Genetic Options: Available only when Genetic Optimization is selected. These options allow users to fine-tune the genetic algorithm's parameters, such as the number of generations, population size, crossover probability, mutation probability, number of islands, migration interval, and population migration rate.

Summary

The Optimization Type section in the Portfolio Builder settings allows users to choose between Simple Optimization and Genetic Optimization. Simple Optimization provides a comprehensive search through all parameter combinations, suitable for smaller datasets. In contrast, Genetic Optimization leverages evolutionary algorithms to efficiently explore larger parameter spaces, making it ideal for more complex optimization tasks. By selecting the appropriate optimization type, users can balance thoroughness and efficiency in constructing their optimal portfolio.