Grammar-based generation of stochastic local search heuristics through automatic algorithm configuration tools

Several grammar-based genetic programming algorithms have been proposed in the literature to automatically generate heuristics for hard optimization problems. These approaches specify the algorithmic building blocks and the way in which they can be combined in a grammar; the best heuristic for the p...

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Veröffentlicht in:Computers & operations research Jg. 51; S. 190 - 199
Hauptverfasser: Mascia, Franco, López-Ibáñez, Manuel, Dubois-Lacoste, Jérémie, Stützle, Thomas
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kidlington Elsevier Ltd 01.11.2014
Elsevier
Pergamon Press Inc
Schlagworte:
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
Automatic algorithm configuration
Bin packing
Computer science; control theory; systems
Exact sciences and technology
Flows in networks. Combinatorial problems
Flowshop scheduling
Grammar
Grammatical evolution
Heuristic
Heuristics
Integer programming
Operational research and scientific management
Operational research. Management science
Optimization algorithms
Packing problem
Scheduling, sequencing
Stochastic models
Studies
Theoretical computing
Automatic algorithm configuration
Bin packing
Grammatical evolution
Flowshop scheduling
Heuristics
Local search
Permutation
Evolutionary algorithm
Algorithmics
Bin packing problem
Scheduling
Grammar
Combinatorial optimization
Delay
Search algorithm
Genetic algorithm
Heuristic method
Flow shop
Satisfiability
Mathematical programming
ISSN:0305-0548, 1873-765X, 0305-0548
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Zusammenfassung:Several grammar-based genetic programming algorithms have been proposed in the literature to automatically generate heuristics for hard optimization problems. These approaches specify the algorithmic building blocks and the way in which they can be combined in a grammar; the best heuristic for the problem being tackled is found by an evolutionary algorithm that searches in the algorithm design space defined by the grammar. In this work, we propose a novel representation of the grammar by a sequence of categorical, integer, and real-valued parameters. We then use a tool for automatic algorithm configuration to search for the best algorithm for the problem at hand. Our experimental evaluation on the one-dimensional bin packing problem and the permutation flowshop problem with weighted tardiness objective shows that the proposed approach produces better algorithms than grammatical evolution, a well-established variant of grammar-based genetic programming. The reasons behind such improvement lie both in the representation proposed and in the method used to search the algorithm design space.
Bibliographie:SourceType-Scholarly Journals-1
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content type line 14
ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2014.05.020