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Dialectic Search
- CP 09, Springer LNCS 5732
"... Abstract. We introduce Hegel and Fichte’s dialectic as a search meta-heuristic for constraint satisfaction and optimization. Dialectic is an appealing mental concept for local search as it tightly integrates and yet clearly marks off of one another the two most important aspects of local search algo ..."
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Abstract. We introduce Hegel and Fichte’s dialectic as a search meta-heuristic for constraint satisfaction and optimization. Dialectic is an appealing mental concept for local search as it tightly integrates and yet clearly marks off of one another the two most important aspects of local search algorithms, search space exploration and exploitation. We believe that this makes dialectic search easy to use for general computer scientists and non-experts in optimization. We illustrate dialectic search, its simplicity and great efficiency on four problems from three different problem domains: constraint satisfaction, continuous optimization, and combinatorial optimization. 1
Constraints manuscript No. (will be inserted by the editor) A Constraint-Based Local Search
"... the date of receipt and acceptance should be inserted later Abstract MiniZinc is a modelling language for combinatorial problems, which can then be solved by a solver provided in a backend. There are many backends, based on technologies such as constraint programming, integer programming, or Boolean ..."
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the date of receipt and acceptance should be inserted later Abstract MiniZinc is a modelling language for combinatorial problems, which can then be solved by a solver provided in a backend. There are many backends, based on technologies such as constraint programming, integer programming, or Boolean satisfiability solving. However, to the best of our knowledge, there is currently no constraint-based local search (CBLS) backend. We discuss the challenges to develop such a backend and give an overview of the design of a CBLS backend for MiniZinc. Experimental results show that for some MiniZinc models, our CBLS backend, based on the OscaR/CBLS solver, is able to give good-quality results in short time.
