AM-77 - Genetic algorithms and global solutions

You are currently viewing an archived version of Topic Genetic algorithms and global solutions. If updates or revisions have been published you can find them at Genetic algorithms and global solutions.

Learning Objectives: 
  • 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