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  • Influence
Modern heuristic techniques for combinatorial problems
Part 1 Introduction: combinatorial problems local and global optima heuristics. Part 2 Simulated annealing: the basic method enhancements and modifications applications conclusions. Part 3 TabuExpand
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A genetic algorithm for flowshop sequencing
  • C. Reeves
  • Mathematics, Computer Science
  • Comput. Oper. Res.
  • 1995
A Genetic Algorithm is developed for finding the minimum makespan of the n-job, m-machine permutation flowshop sequencing problem. Expand
  • 790
  • 64
Genetic Algorithms: Principles and Perspectives: A Guide to Ga Theory
1. Introduction. 2. Basic Principles. 3. Schema Theory. 4. Non Free Lunch for GAs. 5. GAs as Markov Processes. 6. The Dynamical Systems Model. 7. Statistical Mechanics Approximations. 8. PredictingExpand
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  • 34
  • Open Access
Constructive and composite heuristic solutions to the P// Sigma Ci scheduling problem
  • J. Liu, C. Reeves
  • Mathematics, Computer Science
  • Eur. J. Oper. Res.
  • 16 July 2001
We propose a new constructive heuristic procedure to solve the permutation flowshop scheduling problem with the criterion of minimising the total flow time. Expand
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Genetic Algorithms—Principles and Perspectives
  • C. Reeves, J. Rowe
  • Computer Science
  • Operations Research/Computer Science Interfaces…
  • 2002
  • 197
  • 22
  • Open Access
Evolutionary computation: a unified approach
  • C. Reeves
  • Computer Science
  • Genetic Programming and Evolvable Machines
  • 1 September 2007
This is Ken De Jong’s first book on evolutionary computation, and it is a superb introduction to the field. Expand
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Genetic Algorithms for the Operations Researcher
  • C. Reeves
  • Computer Science
  • INFORMS J. Comput.
  • 1 August 1997
Genetic algorithms have become increasingly popular as a means of solving hard combinatorial optimization problems of the type familiar in operations research. Expand
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  • 12
Genetic Algorithms, Path Relinking, and the Flowshop Sequencing Problem
In a previous paper, a simple genetic algorithm (GA) was developed for finding (approximately) the minimum makespan of the n-job, m-machine permutation flowshop sequencing problem (PFSP) was comparable to that of a naive neighborhood search technique and a proven simulated annealing algorithm. Expand
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  • Open Access
Using Genetic Algorithms with Small Populations
We consider applications where it is important to use as small a population as possible, where the number of tness evaluations is limited, and where non-binary representations are important. Expand
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Modern Heuristic Search Methods
Localized Annealing in Constraint Satisfaction and Optimization with Genetic Algorithms . Expand
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