In Modern Heuristic Techniques for Combinatorial Problems

By Fred W. Glover, Manuel Laguna. 1993

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Citations in the corpus

Accelerating Very Deep Convolutional Networks for Classification and Detection (2015, background): “The problem in (14) is a combinatorial problem [37].”
Efficient and accurate approximations of nonlinear convolutional networks (2014, background): “The problem in (14) is a combinatorial problem [15].”
Stochastic Optimization: a Review (2002, methodology): “Aarts & Korst (1989), Davis (1991), Glover (1990b), and Reeves (1995b) were particularly influential in forming our views on the comparative merits of the stochastic optimization methods we examine.”
Learning Evaluation Functions to Improve Optimization by Local Search (2000, methodology): “…local search algorithms attempt to exploit Obj's local structure to locate good approximate optima; these include iterative improvement (hillclimbing), simulated annealing (Kirkpatrick, Gelatt, & Vecchi, 1983), tabu search (Glover & Laguna, 1993), and Walksat (Selman, Kautz, & Cohen, 1996).”
Transit network design and scheduling: A global review (2008, methodology): “For this reason, a large number of reviewed papers, in particular the ones published in recent years, use solution methods based on heuristics and metaheuristics ( Reeves, 1993 ).”
The Measure of Pareto Optima (2003, background): “Indeed, the very elements of the GAs, in particular the crossover operator, lend themselves toward propagating those features in a chromosome that tend to be associated with high fitness values [10].”
An MCDM approach to portfolio optimization (2004, methodology): “We refer for example, to the texts by Reeves (1993), Rayward-Smith et al. (1996), or Aarts and Lenstra (1997) for details on metaheuristics.”
Software release planning: an evolutionary and iterative approach (2004, background): “Some have suggested higher populations [10] while others indicate that population sizes as low as thirty are adequate [21].”
Permutation flow shop scheduling with earliness and tardiness penalties (2004, methodology): “In the second step (Step 6) we use a Tabu Search procedure (Reeves 1995) to perform a local neighbourhood search.”
Large Language Models as Optimizers (2023, methodology): “Many optimization techniques are iterative: the optimization starts from an initial solution, then iteratively updates the solution to optimize the objective function (Amari, 1993; Qian, 1999; Kingma & Ba, 2015; Bäck & Schwefel, 1993; Rios & Sahinidis, 2013; Reeves, 1993).”
Principles of scatter search (2006, methodology): “Laguna and Martı́ (2003) propose a diversification generator method for nonlinear function optimization in a continuous solution space.”
Efficiently Solving the Redundancy Allocation Problem Using Tabu Search (2003, methodology): “General material and additional background information on TS is available from several references including Glover (1989, 1990), Glover et al. (1993), and Reeves (1993) the most comprehensive source, however, being Glover and Laguna (1997).”
Ant Colony Optimization (2001, unknown): “ASrankCVRPsav was compared in Reimann et al. (2002b) and in a recent follow-up paper (Reimann, Doerner, & Hartl, 2004) to several tabu search algorithms for this problem, showing that it performs better than earlier tabu search algorithms.”
Population-based methods in the optimization of stand management. (2009, methodology): “Evolution strategy resembles genetic algorithms (Reeves 1993) in the sense that recombination and mutation are used to produce offspring.”
Hybrid metaheuristics in combinatorial optimization: A survey (2011, methodology): “The origins of metaheuristics are to be found in the Artiﬁcial Intelligence and Operations Research communities [1, 2, 3].”