A policy-based learning beam search for combinatorial optimization

DOI10.1007/978-3-031-30035-6_9MaRDI QIDQ6149097

Marc Huber, Rupert Ettrich, Günther R. Raidl

Publication date: 12 January 2024

Published in: Evolutionary Computation in Combinatorial Optimization (Search for Journal in Brave)

zbMATH Keywords

beam searchreinforcement learningmachine learninglongest common subsequence problem

Mathematics Subject Classification ID

Evolutionary algorithms, genetic algorithms (computational aspects) (68W50) Approximation methods and heuristics in mathematical programming (90C59) Combinatorial optimization (90C27)

Cites Work

• Finding the longest common subsequence for multiple biological sequences by ant colony optimization
• A large neighborhood search heuristic for the longest common subsequence problem
• The Complexity of Some Problems on Subsequences and Supersequences
• Algorithms on Strings, Trees and Sequences
• A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
• Unnamed Item

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