genetic algorithms (GA)

AM-78 - Genetic algorithms and artificial genomes
  • Create an artificial genome that can be used in a genetic algorithm to solve a specific problem
  • Describe a cluster in a way that could be represented in a genome
  • Explain how and why the representation of a GA’s chromosome strings can enhance or hinder the effectiveness of the GA
  • Use one of the many freely available GA packages to apply a GA to implement a simple genetic algorithm to a simple problem, such as optimizing the location of one or more facilities or optimizing the selection of habitat for a nature preserve geospatial pattern optimization (such as for finding clusters of disease points)
  • Describe a potential solution for a problem in a way that could be represented in a chromosome and evaluated according to some measure of fitness (such as the total distance everyone travels to the facility or the diversity of plants and animals that would be protected) genome
AM-77 - Genetic algorithms and global solutions
  • 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
AM-78 - Genetic algorithms and artificial genomes
  • Create an artificial genome that can be used in a genetic algorithm to solve a specific problem
  • Describe a cluster in a way that could be represented in a genome
  • Explain how and why the representation of a GA’s chromosome strings can enhance or hinder the effectiveness of the GA
  • Use one of the many freely available GA packages to apply a GA to implement a simple genetic algorithm to a simple problem, such as optimizing the location of one or more facilities or optimizing the selection of habitat for a nature preserve geospatial pattern optimization (such as for finding clusters of disease points)
  • Describe a potential solution for a problem in a way that could be represented in a chromosome and evaluated according to some measure of fitness (such as the total distance everyone travels to the facility or the diversity of plants and animals that would be protected) genome
AM-77 - Genetic algorithms and global solutions
  • 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
AM-78 - Genetic algorithms and artificial genomes
  • Create an artificial genome that can be used in a genetic algorithm to solve a specific problem
  • Describe a cluster in a way that could be represented in a genome
  • Explain how and why the representation of a GA’s chromosome strings can enhance or hinder the effectiveness of the GA
  • Use one of the many freely available GA packages to apply a GA to implement a simple genetic algorithm to a simple problem, such as optimizing the location of one or more facilities or optimizing the selection of habitat for a nature preserve geospatial pattern optimization (such as for finding clusters of disease points)
  • Describe a potential solution for a problem in a way that could be represented in a chromosome and evaluated according to some measure of fitness (such as the total distance everyone travels to the facility or the diversity of plants and animals that would be protected) genome
AM-77 - Genetic algorithms and global solutions
  • 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
AM-78 - Genetic algorithms and artificial genomes
  • Create an artificial genome that can be used in a genetic algorithm to solve a specific problem
  • Describe a cluster in a way that could be represented in a genome
  • Explain how and why the representation of a GA’s chromosome strings can enhance or hinder the effectiveness of the GA
  • Use one of the many freely available GA packages to apply a GA to implement a simple genetic algorithm to a simple problem, such as optimizing the location of one or more facilities or optimizing the selection of habitat for a nature preserve geospatial pattern optimization (such as for finding clusters of disease points)
  • Describe a potential solution for a problem in a way that could be represented in a chromosome and evaluated according to some measure of fitness (such as the total distance everyone travels to the facility or the diversity of plants and animals that would be protected) genome
AM-77 - Genetic algorithms and global solutions
  • 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
AM-78 - Genetic algorithms and artificial genomes
  • Create an artificial genome that can be used in a genetic algorithm to solve a specific problem
  • Describe a cluster in a way that could be represented in a genome
  • Explain how and why the representation of a GA’s chromosome strings can enhance or hinder the effectiveness of the GA
  • Use one of the many freely available GA packages to apply a GA to implement a simple genetic algorithm to a simple problem, such as optimizing the location of one or more facilities or optimizing the selection of habitat for a nature preserve geospatial pattern optimization (such as for finding clusters of disease points)
  • Describe a potential solution for a problem in a way that could be represented in a chromosome and evaluated according to some measure of fitness (such as the total distance everyone travels to the facility or the diversity of plants and animals that would be protected) genome
AM-77 - Genetic algorithms and global solutions
  • 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

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