Genetic Algorithm (GA) is a heuristic optimization technique used in artificial intelligence to solve complex problems. It is based on the principles of natural selection; i.e., the survival of the fittest, and was inspired by the process of natural evolution. GAs are useful in a wide range of applications, particularly those related to financial management.
Overview
Genetic Algorithms are based on the process of natural selection and take inspiration from Charles Darwin’s theory of evolution. GAs begin with a population of solutions and apply the principles of selection, crossover and mutation to it in order to search for an optimal solution to a particular problem.
The process begins with the initialization of a population of solutions, also called individuals. These individuals are then assessed using an evaluation function to determine their ‘fitness’, and the fitter individuals are preserved and passed on to the next generation. Each new generation of individuals is further assessed and optimized until a satisfactory solution is found.
The selection process works on the principle of ‘survival of the fittest’; i.e., the fitter individuals are chosen over the weaker, leading to better solutions over successive generations. Crossover and mutation further improve the diversity of solutions available by combining the genetic material of two or more individuals and introducing random mutation.
Key Features and Considerations
Some key features and considerations of Genetic Algorithms include the following:
• GAs are an iterative and heuristic search approach that emulate the processes of natural selection and evolution.
• As the number of individuals and the number of generations grow, the solution quality improves and the process converges towards the optimal solution.
• Crossover and mutation play an important role in the search process, providing greater diversity of solutions.
• The fitness function used to assess each individual’s ‘fitness’ should be carefully chosen for the problem, bearing in mind the desired outcomes.
• The selection of individuals for the next generation should also be chosen with care to balance exploration with exploitation of the existing solutions.
Real-World Applications
Genetic Algorithms are widely used in financial management applications. One example is the selection of a portfolio of stocks based on risk-return criteria. GAs can be used to determine the optimal proportions of each stock in the portfolio, while also taking into account constraints such as risk levels, transaction costs and other factors.
To do this, GAs begin with a population of individuals representing different asset allocations. They are then assessed using a fitness function to determine how well they meet the given objectives, and the fitter individuals are preserved and passed on to the next generation. This process is repeated until an optimal portfolio is obtained.
Conclusion
Genetic Algorithms are a powerful and versatile optimization tool used to address complex problems in artificial intelligence and financial management. By emulating the principles of natural selection, GAs can determine the optimal solution from a given set of individuals, while taking into account pertinent considerations. Through the processes of selection, crossover and mutation, GAs can find the best possible solution for a given problem within a reasonable amount of time.
« Back to Glossary Index