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

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

Decades of techniques in combinatorial challenge fixing have produced larger and extra advanced algorithms. those new tools are higher for the reason that they could resolve higher difficulties and handle new software domain names. also they are extra complicated because of this they're tough to breed and infrequently more durable to fine-tune to the peculiarities of a given challenge. This final element has created a paradox the place effective instruments are out of achieve of practitioners.

Autonomous seek (AS) represents a brand new learn box outlined to exactly handle the above problem. Its significant power and originality consist within the indisputable fact that challenge solvers can now practice self-improvement operations in keeping with research of the performances of the fixing strategy -- together with momentary 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 keep watch over. independent seek "crosses the chasm" and offers engineers and practitioners with platforms which are in a position to autonomously self-tune their functionality whereas successfully fixing difficulties.

This is the 1st booklet 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 keep watch over concept. After the editors' advent to independent seek, the chapters are excited by tuning set of rules parameters, self reliant entire (tree-based) constraint solvers, independent keep an eye on in metaheuristics and heuristics, and destiny self sufficient fixing paradigms.

Autonomous seek (AS) represents a brand new study 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 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 keep an eye on. self sustaining 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 publication 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 keep an eye on thought. After the editors' creation to self sustaining seek, the chapters are keen on tuning set of rules parameters, self reliant entire (tree-based) constraint solvers, self reliant keep watch over in metaheuristics and heuristics, and destiny independent 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 components of constraint programming, laptop studying, evolutionary computing, and suggestions regulate conception. After the editors' creation to self sustaining seek, the chapters are fascinated with tuning set of rules parameters, independent whole (tree-based) constraint solvers, self sustaining keep an eye on 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 excited by tuning set of rules parameters, self sufficient whole (tree-based) constraint solvers, self sustaining keep an eye on in metaheuristics and heuristics, and destiny self sufficient fixing paradigms.

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Combining meta-EAs and racing for difficult EA parameter tuning tasks. In: Lobo, F. , Lima, C. , 121–142. Springer (2007) Chapter 3 Automated Algorithm Configuration and Parameter Tuning Holger H. 1 Introduction Computationally challenging problems arise in the context of many applications, and the ability to solve these as efficiently as possible is of great practical, and often also economic, importance. Examples of such problems include scheduling, time-tabling, resource allocation, production planning and optimisation, computeraided design and software verification.

E. Eiben and S. K. Smit 1. single-stage and 2. multistage procedures. Single-stage procedures perform the same number of tests for each given vector, while multistage procedures use a more sophisticated strategy. In general, they augment the TEST step with a SELECT step, where only promising vectors are selected for further testing and those with low performance are deliberately ignored. 2, some methods are only applicable to quantitative parameters. Sophisticated tuners, such as SPOT [3], however, can be used for quantitative, qualitative or even mixed parameter spaces.

To consider the most important aspects of the parameter tuning problem. 3. To give an overview of existing parameter tuning methods. 4. To elaborate on the methodological issues involved here and provide recommendations for further research. a. crossover) and the selection operators (parent selection and survivor selection); cf. [17]. 1. A decision to use an evolutionary algorithm to solve some problem implies that the user or algorithm designer adopts the main design decisions that led to the general evolutionary algorithm framework and only needs to specify “a few” details.

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