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Abstract:
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In the examination and evolution of genetic algorithms, advancement occurred when utilizing a terrain to model a population of individuals and their associated parameters. This work, detailed by Gordon et al and called TBGA, showed that optimization in traditional CGAs could be improved by this approach. A visualization tool called VisTBGA, developed for viewing the TBGA terrain, was developed for aiding researchers in determining good parameters to utilize with the original CGA.
Later, Azevedo extended the TBGA into a Terrain-Based Memetic Algorithm (TBMA). The TBMA is a diffusion Memetic Algorithm (MA) which utilizes the TBGA concept of spreading parameter values. However, the TBMA's parameter values include local search (LS) step sizes and utilize the 2-dimensional grid for these parameters rather than for evolution parameters.
This project updates the work of VisTBGA to develop a similar visualization tool for the TBMA. The VisTBGA provides visual feedback for researchers, aids with solving problem sets, and provides possibilities for future research into terrain-based approaches to memetic algorithms. |
