Artificial Intelligence Genetic Algorithms and Applications of Genetic Algorithms in Compilers Prasad A. Kulkarni.

Artificial Intelligence

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 Darwin’s 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

Create initialCreate initialpopulation of population of chromosomeschromosomes

Evaluate fitness ofEvaluate fitness ofeach chromosomeeach chromosomein the populationin the population

Terminate Terminate cond. ?cond. ?

Output the Output the bestbest

solution solution foundfound

Perform crossover/Perform crossover/mutation to createmutation to create

new generationnew generation

YY

NN

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GA Stage 2 – Evaluation and Selection

Evaluate the fitness value of each chromosome.

Create initialCreate initialpopulation of population of chromosomeschromosomes

Evaluate fitness ofEvaluate fitness ofeach chromosomeeach chromosomein the populationin the population

Terminate Terminate cond. ?cond. ?

Output the Output the bestbest

solution solution foundfound

Perform crossover/Perform crossover/mutation to createmutation to create

new generationnew generation

YY

NN

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GA Stage 3 – Check Termination

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

Create initialCreate initialpopulation of population of chromosomeschromosomes

Evaluate fitness ofEvaluate fitness ofeach chromosomeeach chromosomein the populationin the population

Terminate Terminate cond. ?cond. ?

Output the Output the bestbest

solutionsolutionfoundfound

Perform crossover/Perform crossover/mutation to createmutation to create

new generationnew generation

YY

NN

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GA Stage 4 – Reproduction

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

Create initialCreate initialpopulation ofpopulation ofchromosomeschromosomes

Evaluate fitness ofEvaluate fitness ofeach chromosomeeach chromosomein the populationin the population

Terminate Terminate cond. ?cond. ?

Output the Output the bestbest

solution solution foundfound

Perform crossover/Perform crossover/mutation to createmutation to create

new generationnew generation

YY

NN

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GA Stage 5 – Output Solution

Output the best solution found during the algorithm.

Create initialCreate initialpopulation ofpopulation ofchromosomeschromosomes

Evaluate fitness ofEvaluate fitness ofeach chromosomeeach chromosomein the populationin the population

Terminate Terminate cond. ?cond. ?

Output the Output the bestbest

solution solution foundfound

Perform crossover/Perform crossover/mutation to createmutation to create

new generationnew generation

YY

NN

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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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population1

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GA Stage 2 – Evaluation and Selection

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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.