Genetic Algorithms (GA) are a powerful search and optimization technique that can be applied tonumerous problems. Unfortunately, GA relies on large numbers of fitness evaluations todetermine the relative merits of various solutions to a problem. For problems requiringcomputationally intensive fitness evaluations this can make GA too expensive to use. We describe ahierarchical technique that we have created called Multi-Grid Genetic Algorithms (MGGA). MGGA leverages the geometry of a problem space to build a hierarchy of increasingly smaller problem spaces. Optimizations over these smaller spaces are used to seed a population of solutions in a larger space. We explore how MGGA can be applied to several radiation shielding problems.
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Multi-Grid Genetic Algorithms For Optimal Radiation Shield Design.