Genetic Algorithms and Applications of Genetic Algorithms in Compilers

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Genetic Algorithms and Applications of Genetic
Algorithms in Compilers

• Prasad A. Kulkarni

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Search Strategies in AI

• Searching is a very important application area of
  artificial intelligence.
• Common search strategies
• depth first search
• breadth first search
• best first search
• hill climbing
• simulated annealing
• genetic algorithms

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Outline

• Introduction and historical background
• Biological background
• Stages in a genetic algorithm
• Phase ordering problem in compilers
• Applying GA to address phase ordering
• Other GA applications
• Conclusions

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Introduction and Historical Background

• GAs are part of evolutionary computing introduced
  by I. Rechenberg in the 1960s.
• GAs were invented by John Holland in 1975.
• GAs are based on Darwins principle of evolution
  and survival of the fittest.
• GA is a biased sampling search technique.

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Outline

• Introduction and historical background
• Biological background
• Stages in a genetic algorithm
• Phase ordering problem in compilers
• Applying GA to address phase ordering
• Other GA applications
• Conclusions

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Biological Background
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Biological Background (cont...)

• Crossover, mutation

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Outline

• Introduction and historical background
• Biological background
• Stages in a genetic algorithm
• Phase ordering problem in compilers
• Applying GA to address phase ordering
• Other GA applications
• Conclusions

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GA Stage 1 Initialization

• Randomly initialize some number of individual
  solutions to form an initial population

Output the best solution found
Create initial population of chromosomes
Evaluate fitness of each chromosome in the
population
Y
Terminate cond. ?
N
Perform crossover/ mutation to create new
generation
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GA Stage 2 Evaluation and Selection

• Evaluate the fitness value of each chromosome.

Create initial population of chromosomes
Output the best solution found
Evaluate fitness of each chromosome in the
population
Y
Terminate cond. ?
N
Perform crossover/ mutation to create new
generation
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GA Stage 3 Check Termination

• The algorithm terminates when some pre-determined
  termination condition is reached.

Create initial population of chromosomes
Output the best solution found
Evaluate fitness of each chromosome in the
population
Y
Terminate cond. ?
N
Perform crossover/ mutation to create new
generation
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GA Stage 4 Reproduction

• Perform crossover and mutation to generate a new
  population from the current chromosomes.

Create initial population of chromosomes
Output the best solution found
Evaluate fitness of each chromosome in the
population
Y
Terminate cond. ?
N
Perform crossover/ mutation to create new
generation
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GA Stage 5 Output Solution

• Output the best solution found during the
  algorithm.

Create initial population of chromosomes
Output the best solution found
Evaluate fitness of each chromosome in the
population
Y
Terminate cond. ?
N
Perform crossover/ mutation to create new
generation
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Outline

• Introduction and historical background
• Biological background
• Stages in a genetic algorithm
• Phase ordering problem in compilers
• Applying GA to address phase ordering
• Other GA applications
• Conclusions

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Phase Ordering Problem

• Optimizing compilers apply several optimization
  phases to improve the performance of
  applications.
• Optimization phases interact with each other.
• Determining the best order of applying
  optimization phases has been a long standing
  problem in compilers.

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Addressing the Phase Ordering Problem

• Most researchers consider exhaustive enumeration
  infeasible
• largest optimization sequence length in our
  experiments was 32
• 15 phases for an optimization sequence length of
  32 would result in 1532 evaluations!
• Heuristics are used to search only a small
  portion of the optimization phase order space
• random search, genetic algorithms etc.

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Outline

• Introduction and historical background
• Biological background
• Stages in a genetic algorithm
• Phase ordering problem in compilers
• Applying GA to address phase ordering
• Other GA applications
• Conclusions

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GA Stage 1 Initialization
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GA Stage 2 Evaluation and Selection
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95
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GA Stage 3 Check Termination

• Terminating conditions
• the desired performance is reached
• specified number of generations have elapsed
• time constraint for genetic algorithm exceeded

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GA Stage 4 Reproduction

• Crossover - 20 sequences in each generation
  replaced

• Mutation - phases in each sequence replaced with
  a low probability

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Outline

• Introduction and historical background
• Biological background
• Stages in a genetic algorithm
• Phase ordering problem in compilers
• Applying GA to address phase ordering
• Other GA applications
• Conclusions

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Other GA Applications

• Designing electronic circuits
• Designing of aircrafts
• Routing in circuit switched telecommunication
  networks
• Genetic algorithm applied to robot trajectory
  design
• and many more....

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Conclusion

• Genetic algorithm are a class of heuristic biased
  sampling approach to searching large spaces.
• Described steps during a genetic algorithm
• Presented detailed application of genetic
  algorithm to phase ordering problem in compilers.