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Solving machine learning with machine learning: Exploiting Very Large-Scale Neighbourhood Search for synthesizing machine learning pipelines
Solving ML with ML: Effectiveness of a star search for synthesizing machine learning pipelines
Solving ML with ML: Effectiveness of the Metropolis-Hastings algorithm for synthesizing Machine Learning Pipelines
Solving ML with ML: Evaluating the performance of the Monte Carlo Tree Search algorithm in the context of Program Synthesis
How to train your dragon: on the application of the Metropolis-Hastings algorithm for program synthesis
Genetic Algorithms for Inductive Program Synthesis
Improvements in Monte Carlo Tree Search for Inductive Program Synthesis
Evolving a Search Procedure for Program Synthesis
Genetic Algorithm for Evolving an Objective Function of a Program Synthesizer
Evolving Design Patterns for Program Synthesis
GENERALIZE: A framework for evolving searching constraints for domain-specific languages in program synthesis
Improving Inductive Program Synthesis by using Very Large Neighborhood Search and Variable-Depth Neighborhood Search
Inductive Program Synthesis through using Monte Carlo Tree Search guided by a heuristic-based loss function
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