97 research outputs found

    Autonomous Search

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    Description of GADEL

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    This article describes the first implementation of the GADEL system : a Genetic Algorithm for Default Logic. The goal of GADEL is to compute extensions in Reiter's default logic. It accepts every kind of finite propositional default theories and is based on evolutionary principles of Genetic Algorithms. Its first experimental results on certain instances of the problem show that this new approach of the problem can be successful.Comment: System Descriptions and Demonstrations at Nonmonotonic Reasoning Workshop, 2000 6 pages, 2 figures, 5 table

    Vers des algorithmes plus autonomes

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    On Implementation of Tree Synchronized Languages

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    A Unified Framework to Compute over Tree Synchronized Grammars and Primal Grammars

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    Tree languages are powerful tools for the representation and schematization of infinite sets of terms for various purposes (unification theory, verification and specification ...). In order to extend the regular tree language framework, more complex formalisms have been developed. In this paper, we focus on Tree Synchronized Grammars and Primal Grammars which introduce specific control structures to represent non regular sets of terms. We propose a common unified framework in order to achieve the membership test for these particular languages. Thanks to a proof system, we provide a full operational framework, that allows us to transform tree grammars into Prolog programs (as it already exists for word grammars with DCG) whose goal is to recognize terms of the corresponding language

    GASAT: A Genetic Local Search Algorithm for the Satisfiability Problem

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    Autonomous operator management for evolutionary algorithms

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    The performance of an evolutionary algorithm strongly depends on the design of its operators and on the management of these operators along the search; that is, on the ability of the algorithm to balance exploration and exploitation of the search space. Recent approaches automate the tuning and control of the parameters that govern this balance. We propose a new technique to dynamically control the behavior of operators in an EA and to manage a large set of potential operators. The best operators are rewarded by applying them more often. Tests of this technique on instances of 3-SAT return results that are competitive with an algorithm tailored to the problem

    On Migration Policies in Dynamic Island Models

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