Search Results - Buzdalova, A S

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  1. 1
    Adaptive selection of auxiliary objectives in multiobjective evolutionary algorithms

    Adaptive selection of auxiliary objectives in multiobjective evolutionary algorithms by Petrova, I.A., Buzdalova, A.S., Shalyto, A.A.

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  2. 2
    ADAPTIVE SELECTION OF ARTIFICIAL IMMUNE SYSTEMS AND LOCAL SEARCH MUTATION OPERATORS

    ADAPTIVE SELECTION OF ARTIFICIAL IMMUNE SYSTEMS AND LOCAL SEARCH MUTATION OPERATORS by Bulanova, N S, Buzdalova, A S, Shalyto, A A

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  3. 3
    THEORETICAL ANALYSIS OF DYNAMIC SELECTION OF SWITCHING AUXILIARY OBJECTIVES ON XdivK PROBLEM

    THEORETICAL ANALYSIS OF DYNAMIC SELECTION OF SWITCHING AUXILIARY OBJECTIVES ON XdivK PROBLEM by Petrova, I A, Buzdalova, A S, Shalyto, A A

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  4. 4
    ADAPTIVE SELECTION OF AUXILIARY OBJECTIVES IN MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS

    ADAPTIVE SELECTION OF AUXILIARY OBJECTIVES IN MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS by Petrova, I A, Buzdalova, A S, Shalyto, A A

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  5. 5
    EFFICIENCY INCREASING METHOD OF THE EVOLUTIONARY ALGORITHMS BY REINFORCEMENT LEARNING

    EFFICIENCY INCREASING METHOD OF THE EVOLUTIONARY ALGORITHMS BY REINFORCEMENT LEARNING by Buzdalova, A S, Buzdalov, M

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  6. 6
    Can OneMax help optimizing LeadingOnes using the EA+RL method?

    Can OneMax help optimizing LeadingOnes using the EA+RL method? by Buzdalov, Maxim, Buzdalova, Arina

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  7. 7
    Analysis of Q-learning with random exploration for selection of auxiliary objectives in random local search

    Analysis of Q-learning with random exploration for selection of auxiliary objectives in random local search by Buzdalov, Maxim, Buzdalova, Arina

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  8. 8
    Adaptive selection of helper-objectives for test case generation

    Adaptive selection of helper-objectives for test case generation by Buzdalov, Maxim, Buzdalova, Arina

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  9. 9
    Adaptive selection of artificial immune systems and local search mutation operators

    Adaptive selection of artificial immune systems and local search mutation operators by Bulanova, N.S., Buzdalova, A.S., Shalyto, A.A.

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  10. 10
    Theoretical analysis of dynamic selection of switching auxiliary objectives on XdivK problem

    Theoretical analysis of dynamic selection of switching auxiliary objectives on XdivK problem by Petrova, I.A., Buzdalova, A.S., Shalyto, A.A.

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  11. 11
    Fast Re-Optimization of LeadingOnes with Frequent Changes

    Fast Re-Optimization of LeadingOnes with Frequent Changes by Bulanova, Nina, Buzdalova, Arina, Doerr, Carola

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  12. 12
    Increasing Efficiency of Evolutionary Algorithms by Choosing between Auxiliary Fitness Functions with Reinforcement Learning

    Increasing Efficiency of Evolutionary Algorithms by Choosing between Auxiliary Fitness Functions with Reinforcement Learning by Buzdalova, A., Buzdalov, M.

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  13. 13
    Offspring Population Size Matters when Comparing Evolutionary Algorithms with Self-Adjusting Mutation Rates

    Offspring Population Size Matters when Comparing Evolutionary Algorithms with Self-Adjusting Mutation Rates by Rodionova, Anna, Antonov, Kirill, Buzdalova, Arina, Doerr, Carola

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  14. 14
    Reinforcement Learning Based Dynamic Selection of Auxiliary Objectives with Preserving of the Best Found Solution

    Reinforcement Learning Based Dynamic Selection of Auxiliary Objectives with Preserving of the Best Found Solution by Petrova, Irina, Buzdalova, Arina

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  15. 15
    Adaptive Selection of Helper-Objectives with Reinforcement Learning

    Adaptive Selection of Helper-Objectives with Reinforcement Learning by Buzdalova, A., Buzdalov, M.

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  16. 16
    Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm

    Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm by Buzdalova, Arina, Doerr, Carola, Rodionova, Anna

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  17. 17
    Blending Dynamic Programming with Monte Carlo Simulation for Bounding the Running Time of Evolutionary Algorithms

    Blending Dynamic Programming with Monte Carlo Simulation for Bounding the Running Time of Evolutionary Algorithms by Antonov, Kirill, Buzdalov, Maxim, Buzdalova, Arina, Doerr, Carola

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  18. 18
    Adaptive Parameter Selection in Evolutionary Algorithms by Reinforcement Learning with Dynamic Discretization of Parameter Range

    Adaptive Parameter Selection in Evolutionary Algorithms by Reinforcement Learning with Dynamic Discretization of Parameter Range by Rost, Arkady, Petrova, Irina, Buzdalova, Arina

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