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Genetic Algorithms for Topical Web Search: A Study of Different Mutation Rates

Genetic Algorithms for Topical Web Search: A Study of Different Mutation Rates . Roc ́ ıo L. Cecchini †, Carlos M. Lorenzetti‡, Ana G. Maguitman ‡ and N ́ elida Beatriz Brignole †§ . Agenda. INTRODUCTION GENETIC ALGORITHMS GENETIC ALGORITHMS FOR EXPLORING QUERY SPACE

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Genetic Algorithms for Topical Web Search: A Study of Different Mutation Rates

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  1. Genetic Algorithms for Topical Web Search: A Study of Different Mutation Rates Roc ́ıo L. Cecchini†, Carlos M. Lorenzetti‡, Ana G. Maguitman‡ and N ́elida Beatriz Brignole†§

  2. Agenda • INTRODUCTION • GENETIC ALGORITHMS • GENETIC ALGORITHMS FOR EXPLORING QUERY SPACE • SYSTEMARCHITECTURE • THE EFFECT OF DIFFERENT MUTATION RATES • CONCLUDING REMARKS

  3. INTRODUCTION • Harvesting topical content is a process that can be done by formulating topic-relevant queries and submitting them to a search engine. • In this scenario, selecting good query terms can be seen as an optimization problem where the objective function to be optimized is based on the effectiveness of a query to retrieve relevant material.

  4. INTRODUCTION • Solving this optimization problem is challenging because the query space has a huge number of dimensions. • TouseGAtosolveit,becausefollowingcharacteristics: • 1.It is usually sufficient to identify suboptimal solutions • 2. we may be interested in finding many high-quality queries rather than a single one.

  5. GENETIC ALGORITHMS • Selection:Parents are selected to produce offspring, favoring those parents with highest values of the fitness function. • Crossover:Crossover of population members takes place by exchanging subparts of the parent chromosomes • Mutation:mutation is the result of a random perturbation of the chromosome (e.g., replacing a gene by another)

  6. GENETIC ALGORITHMS • Step 1: Start with a randomly generated population • Step 2: Evaluate the fitness of each individual in the population • Step 3: Select individuals to reproduce based on their fitness • Step 4: Apply crossover with probability Pc • Step 5: Apply mutation with probability Pm • Step 6: Replace the population by the new generation of individuals Step • 7: Go to step 2

  7. Selection-fitnessfunction

  8. Selection

  9. Crossover • The crossover operator used in our proposal is known as single-point. • new queries: • 1.the first n terms are contributed by one parent • 2.the remaining terms by the second parent, where the crossover point n is chosen at random.

  10. Mutation • These changes consist in replacing a randomly selected query term t1by another term t2. The term t2is obtained from a mutation pool . • Mutation pool:is updated with new terms from the snippets returned by the search engine

  11. SYSTEMARCHITECTURE

  12. THE EFFECT OF DIFFERENT MUTATION RATES

  13. Evaluationofqueryquality

  14. Conclusion • 1. The techniques presented in this paper are applicable to any domain for which it is possible to generate term-based characterizations of a topic. • 2. higher mutations rates induce a more thorough exploration of the search space • 3. As part of our future work we expect to continue testing different settings for the GA parameters (population size, crossover probability, mutation probability) as well as other selection methods.

  15. Q&A

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