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Handcrafting heuristics for solving complex optimization tasks (e.g., route planning and task allocation) is a common practice but requires extensive domain knowledge.
Multi-task learning for routing problem with cross-problem zero-shot generalization
Liu, F., Lin, X., Wang, Z., Zhang, Q., Xialiang, T., and Yuan, M · 1908
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An effective heuristic algorithm for the traveling-salesman problem
Lin, S. and Kernighan, B. W · 1973
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On bayesian methods for seeking the extremum
Mockus, J · 1974
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An analysis of several heuristics for the traveling salesman problem
Rosenkrantz, D. J., Stearns, R. E., and Lewis, II, P. M · 1977
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The traveling salesman problem a guided tour of combinatorial optimization, 1986
Biggs, N · 1986
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Tsplib—a traveling salesman problem library
Reinelt, G · 1991
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Heuristic algorithm for scheduling in a flowshop to minimize total flowtime
Rajendran, C · 1993
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Handbook of evolutionary computation
Bäck, T., Fogel, D. B., and Michalewicz, Z · 1997
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Genetic local search for the tsp: New results
Merz, P. and Freisleben, B · 1997
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A survey of parallel genetic algorithms
Cantú-Paz, E. et al · 1998
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Guided local search and its application to the traveling salesman problem
Voudouris, C. and Tsang, E · 1999
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Cost-aware bayesian optimization
Lee, E. H., Perrone, V., Archambeau, C., and Seeger, M · 2003
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Ant colony optimization
Dorigo, M., Birattari, M., and Stutzle, T · 2006
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Bandit based monte-carlo planning
Kocsis, L. and Szepesvári, C · 2006
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Gaussian processes for machine learning , volume 2
Williams, C. K. and Rasmussen, C. E · 2006
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Computing “elo ratings” of move patterns in the game of go
Coulom, R · 2007
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Exact methods for the asymmetric traveling salesman problem
Fischetti, M., Lodi, A., and Toth, P · 2007
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An exact method for the double tsp with multiple stacks
Lusby, R. M., Larsen, J., Ehrgott, M., and Ryan, D · 2010
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Heuristic algorithm for priority traffic signal control
He, Q., Head, K. L., and Ding, J · 2011
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Combinatorial optimization , volume 1
Korte, B. H., Vygen, J., Korte, B., and Vygen, J · 2011
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Complexity and approximation: Combinatorial optimization problems and their approximability properties
Ausiello, G., Crescenzi, P., Gambosi, G., Kann, V., Marchetti-Spaccamela, A., and Protasi, M · 2012
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A survey of monte carlo tree search methods
Browne, C. B., Powley, E., Whitehouse, D., Lucas, S. M., Cowling, P. I., Rohlfshagen, P., Tavener, S., Perez, D., Samothrakis, S., and Colton, S · 2012
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Weibull-based benchmarks for bin packing
Castiñeiras, I., De Cauwer, M., and O’Sullivan, B · 2012
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Practical bayesian optimization of machine learning algorithms
Snoek, J., Larochelle, H., and Adams, R. P · 2012
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Hyper-heuristics: A survey of the state of the art
Burke, E. K., Gendreau, M., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., and Qu, R · 2013
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Foundations of genetic programming
Langdon, W. B. and Poli, R · 2013
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A hybrid genetic algorithm based on harmony search and its improving
Shi, W. W., Han, W., and Si, W. C · 2013
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The approximation ratio of the greedy algorithm for the metric traveling salesman problem
Brecklinghaus, J. and Hougardy, S · 2015
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Heuristic and meta-heuristic algorithms and their relevance to the real world: a survey
Desale, S., Rasool, A., Andhale, S., and Rane, P · 2015
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Introduction to evolutionary computing
Eiben, A. E. and Smith, J. E · 2015
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Taking the human out of the loop: A review of bayesian optimization
Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., and De Freitas, N · 2015
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Pointer networks
Vinyals, O., Fortunato, M., and Jaitly, N · 2015
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Neural combinatorial optimization with reinforcement learning
Bello, I., Pham, H., Le, Q. V., Norouzi, M., and Bengio, S · 2016
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Mo-paramils: A multi-objective automatic algorithm configuration framework
Blot, A., Hoos, H. H., Jourdan, L., Kessaci-Marmion, M.-É., and Trautmann, H · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Bayesian optimization with a finite budget: An approximate dynamic programming approach
Lam, R., Willcox, K., and Wolpert, D. H · 2016
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The irace package: Iterated racing for automatic algorithm configuration
López-Ibáñez, M., Dubois-Lacoste, J., Cáceres, L. P., Birattari, M., and Stützle, T · 2016
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Mastering the game of go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al · 2016
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An extension of the lin-kernighan-helsgaun tsp solver for constrained traveling salesman and vehicle routing problems
Helsgaun, K · 2017
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Attention is all you need
A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions
Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al · 2023
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Evolution through large models
Lehman, J., Gordon, J., Jain, S., Ndousse, K., Yeh, C., and Stanley, K. O · 2023
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Algorithm evolution using large language model
Liu, F., Tong, X., Yuan, M., and Zhang, Q · 2023
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Language model crossover: Variation through few-shot prompting
Meyerson, E., Nelson, M. J., Bradley, H., Gaier, A., Moradi, A., Hoover, A. K., and Lehman, J · 2023
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A comprehensive overview of large language models
Naveed, H., Khan, A. U., Qiu, S., Saqib, M., Anwar, S., Usman, M., Akhtar, N., Barnes, N., and Mian, A · 2023
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Attention, learn to solve routing problems!
Kool, W., Van Hoof, H., and Welling, M · 2018
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Moea/d-gls: a multiobjective memetic algorithm using decomposition and guided local search
Alhindi, A., Alhindi, A., Alhejali, A., Alsheddy, A., Tairan, N., and Alhakami, H · 2019
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Knowledge-guided local search for the vehicle routing problem
Arnold, F. and Sörensen, K · 2019
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Efficiently solving very large-scale routing problems
Arnold, F., Gendreau, M., and Sörensen, K · 2019
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A classification of hyper-heuristic approaches: revisited
Burke, E. K., Hyde, M. R., Kendall, G., Ochoa, G., Özcan, E., and Woodward, J. R · 2019
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A gp hyper-heuristic approach for generating tsp heuristics
Duflo, G., Kieffer, E., Brust, M. R., Danoy, G., and Bouvry, P · 2019
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Monte carlo tree search: A review of recent modifications and applications
Świechowski, M., Godlewski, K., Sawicki, B., and Mańdziuk, J · 2023
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System 2 attention (is something you might need too)
Weston, J. and Sukhbaatar, S · 2023
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Pareto improver: Learning improvement heuristics for multi-objective route planning
Zheng, Z., Yao, S., Li, G., Han, L., and Wang, Z · 2023
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Language agent tree search unifies reasoning acting and planning in language models
Zhou, A., Yan, K., Shlapentokh-Rothman, M., Wang, H., and Wang, Y.-X · 2023
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Abgaryan, H., Harutyunyan, A., and Cazenave, T · 2024
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Routefinder: Towards foundation models for vehicle routing problems
Berto, F., Hua, C., Zepeda, N. G., Hottung, A., Wouda, N., Lan, L., Park, J., Tierney, K., and Park, J · 2024
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Vermcts: Synthesizing multi-step programs using a verifier, a large language model, and tree search
Brandfonbrener, D., Henniger, S., Raja, S., Prasad, T., Loughridge, C. R., Cassano, F., Hu, S. R., Yang, J., Byrd, W. E., Zinkov, R., et al · 2024
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Generating code world models with large language models guided by monte carlo tree search
Dainese, N., Merler, M., Alakuijala, M., and Marttinen, P · 2024
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Dat, P. V. T., Doan, L., and Binh, H. T. T · 2024
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Make every move count: Llm-based high-quality rtl code generation using mcts
DeLorenzo, M., Chowdhury, A. B., Gohil, V., Thakur, S., Karri, R., Garg, S., and Rajendran, J · 2024
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Goal: A generalist combinatorial optimization agent learning
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Towards generalizable neural solvers for vehicle routing problems via ensemble with transferrable local policy, 2024
Gao, C., Shang, H., Xue, K., Li, D., and Qian, C · 2024
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Large language models: a comprehensive survey of its applications, challenges, limitations, and future prospects
Hadi, M. U., Al Tashi, Q., Shah, A., Qureshi, R., Muneer, A., Irfan, M., Zafar, A., Shaikh, M. B., Akhtar, N., Wu, J., et al · 2024
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Evolving code with a large language model
Hemberg, E., Moskal, S., and O’Reilly, U.-M · 2024
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Huang, X., Liu, W., Chen, X., Wang, X., Wang, H., Lian, D., Wang, Y., Tang, R., and Chen, E · 2024
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Unco: Towards unifying neural combinatorial optimization through large language model
Jiang, X., Wu, Y., Wang, Y., and Zhang, Y · 2024
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Llms can’t plan, but can help planning in llm-modulo frameworks
Kambhampati, S., Valmeekam, K., Guan, L., Verma, M., Stechly, K., Bhambri, S., Saldyt, L., and Murthy, A · 2024
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Ant colony sampling with gflownets for combinatorial optimization
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Large language models as evolution strategies
Lange, R., Tian, Y., and Tang, Y · 2024
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Large language models as evolutionary optimizers
Liu, S., Chen, C., Qu, X., Tang, K., and Ong, Y.-S · 2024
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Mutual reasoning makes smaller llms stronger problem-solvers
Qi, Z., Ma, M., Xu, J., Zhang, L. L., Yang, F., and Yang, M · 2024
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Mathematical discoveries from program search with large language models
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Reflexion: Language agents with verbal reinforcement learning
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Neuralgls: learning to guide local search with graph convolutional network for the traveling salesman problem
Sui, J., Ding, S., Xia, B., Liu, R., and Bu, D · 2024
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Pre-tokenization of numbers for large language models
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Large language models as optimizers, 2024
Yang, C., Wang, X., Lu, Y., Liu, H., Le, Q. V., Zhou, D., and Chen, X · 2024
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Controlling the mutation in large language models for the efficient evolution of algorithms
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Deep insights into automated optimization with large language models and evolutionary algorithms
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