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- Describe the difficulty of finding globally optimal solutions for problems with many local optima
- Explain how evolutionary algorithms may be used to search for solutions
- Explain the important advantage that GA methods may offer to find diverse near-optimal solutions
- Explain how a GA searches for solutions by using selection proportional to fitness, crossover, and (very low levels of) mutation to fitness criteria and crossover mutation to search for a globally optimal solution to a problem
- Compare and contrast the effectiveness of multiple search criteria for finding the optimal solution with a simple greedy hill climbing approach