Over the years, Evolutionary Computation techniques have been applied to various problems where the solution space is very large and complex, and where the irregularities in the solution space make it difficult to employ conventional optimization procedures to look for the global optimum.
Computational Intelligence techniques like Neural Networks, Evolutionary Algorithms etc. have become extremely popular in the financial markets owing to the great promise they hold and also because of the exceptional results produced by them through experiments and studies conducted worldwide. Financial Engineering problems are usually very complex and biologically inspired heuristic algorithms (based on Darwin’s concepts of natural selection and survival of the fittest) have gained significant importance in this area, especially for making decisions and solving optimization problems.
The author has studied various aspects of Evolutionary Computation along with their applications to financial problems. To test the effectiveness of one such methodology under Evolutionary Computation (Genetic Algorithms), the author implemented a simple genetic algorithm in MATLAB and also experimented with the various parameters involved in Genetic Algorithms to see the variance in results produced by them.Bachelor of Engineerin
