Download Genetic Algorithm(GA)

Genetic Algorithm(GA)
ZhiZhong Zhuang
Genetic Algorithm
• Develop from Darwin’s Theory of Evolution
• A method that searching the optimal solution by simulating the
processing of natural evolution
Darwin’s Theory of Evolution
• Natural Selection
• Chromosome (gene)
• Crossover
• Mutation
Apply to Algorithm
Nature select from initial species
Algorithm select from Initial set
Nature select better species
Algorithm select better solution
Genetic Algorithm - Initialization
• Coding – can not deal with parameters directly
• Setting evolution generation counter t = 0, set the maximum
evolution number T
• Create an initial population which is usually generate
randomly
Genetic Algorithm - Evaluation
• Create fitness function
• Directly affects the performance of Genetic algorithm.
• Calculating fitness value of each individual
Genetic Algorithm - Selection
• Select the individual who has better fitness value
• Eliminate low fitness value individual
• Improve fitness value
Genetic Algorithm - Crossover
• Exchange gene of previous generation with certain
probability
• Get new individual
• Most important part to get better individual
Genetic Algorithm - Mutation
• Small probability event but important
• Get new individual
• Repeat evaluation, selection, crossover, mutation
Genetic Algorithm - End
• The fitness value of the best individual reaches a given
standard value
• The optimal fitness value of individuals and groups are no
longer rising
• Generation counter equal to maximum evolution number T
Features of GA
• Genetic algorithms process multiple individuals of group,
evaluate multiple solution in searching space, reduce the risk
to get local optimal solution
• Evaluate individual by fitness function only, this can make GA
apply widely
• Use different fitness function to get different search
direction
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