| Summary: | One of the most popular optimization algorithms used in engineering applications are based on gradient methods that where its effectiveness depends heavily on the starting point selected as this method utilizes gradient information to obtain an improved solution in the surrounding of the selected starting point. Although some of these approaches have proven to be quite efficient, their application can be quite limited if the problem involved has more than one local optimum as the optimal solution obtained might not be the global optimum depending on the initial point selected. In this project, the genetic algorithm (GA) approach will be explored. This approach which holds a strong resemblance to the principles involved in the biological evolution process has been gaining substantial popularity due to its ability to identify the global optimum regardless of the initial point selected. By integrating the finite element analysis with the GA, the ability of GA in solving problems that require finite element analysis is investigated.
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