Autonomous Search by Youssef Hamadi, Eric Monfroy, Frédéric Saubion (auth.), PDF

By Youssef Hamadi, Eric Monfroy, Frédéric Saubion (auth.), Youssef Hamadi, Eric Monfroy, Frédéric Saubion (eds.)

ISBN-10: 3642214339

ISBN-13: 9783642214332

Decades of techniques in combinatorial challenge fixing have produced larger and extra advanced algorithms. those new tools are higher because they could remedy greater difficulties and handle new software domain names. also they are extra complicated because of this they're not easy to breed and sometimes tougher to fine-tune to the peculiarities of a given challenge. This final aspect has created a paradox the place effective instruments are out of achieve of practitioners.

Autonomous seek (AS) represents a brand new examine box outlined to exactly tackle the above problem. Its significant energy and originality consist within the undeniable fact that challenge solvers can now practice self-improvement operations in keeping with research of the performances of the fixing procedure -- together with non permanent reactive reconfiguration and long term development via self-analysis of the functionality, offline tuning and on-line regulate, and adaptive keep watch over and supervised regulate. independent seek "crosses the chasm" and offers engineers and practitioners with structures which are capable of autonomously self-tune their functionality whereas successfully fixing difficulties.

This is the 1st e-book devoted to this subject, and it may be used as a reference for researchers, engineers, and postgraduates within the parts of constraint programming, desktop studying, evolutionary computing, and suggestions regulate thought. After the editors' advent to independent seek, the chapters are fascinated with tuning set of rules parameters, self sufficient entire (tree-based) constraint solvers, independent keep watch over in metaheuristics and heuristics, and destiny independent fixing paradigms.

Autonomous seek (AS) represents a brand new examine box outlined to exactly tackle the above problem. Its significant energy and originality consist within the indisputable fact that challenge solvers can now practice self-improvement operations in response to research of the performances of the fixing approach -- together with non permanent reactive reconfiguration and long term development via self-analysis of the functionality, offline tuning and on-line keep watch over, and adaptive keep an eye on and supervised regulate. self reliant seek "crosses the chasm" and gives engineers and practitioners with platforms which are capable of autonomously self-tune their functionality whereas successfully fixing difficulties.

This is the 1st ebook devoted to this subject, and it may be used as a reference for researchers, engineers, and postgraduates within the components of constraint programming, computer studying, evolutionary computing, and suggestions keep watch over idea. After the editors' advent to self reliant seek, the chapters are occupied with tuning set of rules parameters, independent whole (tree-based) constraint solvers, self reliant keep watch over in metaheuristics and heuristics, and destiny independent fixing paradigms.

This is the 1st publication devoted to this subject, and it may be used as a reference for researchers, engineers, and postgraduates within the components of constraint programming, desktop studying, evolutionary computing, and suggestions keep watch over concept. After the editors' creation to self sustaining seek, the chapters are considering tuning set of rules parameters, self sustaining whole (tree-based) constraint solvers, self sufficient keep watch over in metaheuristics and heuristics, and destiny self sustaining fixing paradigms.

This is the 1st e-book devoted to this subject, and it may be used as a reference for researchers, engineers, and postgraduates within the parts of constraint programming, laptop studying, evolutionary computing, and suggestions regulate concept. After the editors' advent to self sustaining seek, the chapters are interested by tuning set of rules parameters, self reliant entire (tree-based) constraint solvers, self sustaining regulate in metaheuristics and heuristics, and destiny self sufficient fixing paradigms.

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Smit shops 2009. , 665–674, Berlin, Springer (2009) Schaffer, J. , Caruana, R. , Eshelman, L. : A study of control parameters affecting online performance of genetic algorithms for function optimization. , 51–60,San Francisco, CA, USA, 1989. Morgan Kaufmann Publishers Inc. (1989) Smit, S. , and Eiben, A. : Comparing parameter tuning methods for evolutionary algorithms. , 399–406. IEEE Computational Intelligence Society, IEEE Press (2009) Smit, S. , and Eiben, A. : Using entropy for parameter analysis of evolutionary algorithms.

Hoos efficacy with which a heuristic algorithm solves a given problem instance or class of problem instances. 1 – a widely used, commercial solver for mixed integer programming problems – has recently been demonstrated to vary by up to a factor of over 50 with the settings of 76 user-accessible parameters [42]. A problem routinely encountered by designers as well as end users of parameterised algorithms is that of finding parameter settings (or configurations) for which the empirical performance on a given set of problem instances is optimised.

One argument for not tuning is that a benchmark may be interpreted as a fixed and invariable algorithm that provides the same challenge to every comparison using it. This may sound unreasonable, giving an unfair advantage to the new algorithm in the comparison. However, the current EC research and publication practice ignores the tuning issue completely, hiding the tuning efforts, and thus hiding the possible unfairness of a comparison between a tuned NI-EA and an untuned BM-EA. In this respect, using a tuner for NI-EA and reporting the tuning efforts alongside the performance results is more illustrative than the contents of the majority of publications at present.

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Autonomous Search by Youssef Hamadi, Eric Monfroy, Frédéric Saubion (auth.), Youssef Hamadi, Eric Monfroy, Frédéric Saubion (eds.)


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