Wolf S Search

A book that seeks to remind children that they must live in harmony with nature, and that no one is more important than another.

Metacognition is a set of active mental processes that allows users to monitor, regulate, and direct their personal cognitive strategies. Improving Student Information Search traces the impact of a tutorial on education graduate students’ problem-solving in online research databases. The tutorial centres on idea tactics developed by Bates that represent metacognitive strategies designed to improve information search outcomes. The first half of the book explores the role of metacognition in problem-solving, especially for education graduate students. It also discusses the use of metacognitive scaffolds for improving students’ problem-solving. The second half of the book presents the mixed method study, including the development of the tutorial, its impact on seven graduate students’ search behaviour and outcomes, and suggestions for adapting the tutorial for other users. Provides metacognitive strategies to improve students’ information search outcomes Incorporates tips to enhance database search skills in digital libraries Includes seminal studies on information behaviour

A compelling and detailed account of the search for the Oakland County child killer. A story of tragedy and grief, dead-ends and disappointments.

This book constitutes the refereed proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2009, held in Tübingen, Germany, in April 2009. The 21 revised full papers presented were carefully reviewed and selected from 53 submissions. The papers present the latest research and discuss current developments and applications in metaheuristics - a paradigm to effectively solve difficult combinatorial optimization problems appearing in various industrial, economical, and scientific domains. Prominent examples of metaheuristics are evolutionary algorithms, simulated annealing, tabu search, scatter search, memetic algorithms, variable neighborhood search, iterated local search, greedy randomized adaptive search procedures, estimation of distribution algorithms and ant colony optimization.

The vehicle routing problem (VRP) is one of the most famous combinatorial optimization problems. In simple terms, the goal is to determine a set of routes with overall minimum cost that can satisfy several geographical scattered - mands. A ?eet of vehicles located in one or more depots is available to ful?ll the requests. A large number of variants exist, adding di?erent constraints to the original de?nition. Some examples are related to the number of depots, the ordering for visiting the customers or to time windows specifying a desirable period to arrive to a given location. The original version of this problem was proposed by Dantzig and Ramser in 1959 [1]. In their seminal paper, the authors address the calculation of a set of optimal routes for a ?eet of gasoline delivery trucks. Since then, the VRP has attractedtheattentionofalargenumberofresearchers.Aconsiderablepartofits success is a consequence of its practical interest, as it resembles many real-world problems faced everyday by distribution and transportation companies, just to mention a few applications areas. In this context, the development of e?cient optimization techniques is crucial. They are able to provide new and enhanced solutionstologisticoperations,andmaythereforeleadtoasubstantialreduction in costs for companies. Additionally, and from a research oriented perspective, the VRP is a challenging NP-hard problem providing excellent benchmarks to access the e?ciency of new global optimization algorithms.

Transformative Journey Blind Seer has run at Firekeeper’s side since the wolf-woman first crossed the Iron Mountains into human-held lands. Now it’s her turn to run alongside the blue-eyed wolf as he sets out in search of someone who can teach him how to use his magical gift—on his own unique terms . The pair’s search will take them to the far side of the world in the company of allies who include a young woman scarred by war, a falcon who believes himself a traitor, and an old friend… or possibly enemy. Together they will fight battles from before they were born, climb mountains, cross badlands, eventually unveiling a threat that will reshape not only Blind Seer, but his belief in what he most desires.

This book is dedicated to metaheuristics as applied to vehicle routing problems. Several implementations are given as illustrative examples, along with applications to several typical vehicle routing problems. As a first step, a general presentation intends to make the reader more familiar with the related field of logistics and combinatorial optimization. This preamble is completed with a description of significant heuristic methods classically used to provide feasible solutions quickly, and local improvement moves widely used to search for enhanced solutions. The overview of these fundamentals allows appreciating the core of the work devoted to an analysis of metaheuristic methods for vehicle routing problems. Those methods are exposed according to their feature of working either on a sequence of single solutions, or on a set of solutions, or even by hybridizing metaheuristic approaches with others kind of methods.

This book constitutes the refereed proceedings of the 10th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2010, held in Instanbul, Turkey, in April 2010. The 24 revised full papers presented were carefully reviewed an selected from 69 submissions. The papers present the latest research and discuss current developments and applications in metaheuristics - a paradigm to effectively solve difficult combinatorial optimization problems appearing in various industrial, economical, and scientific domains. Prominent examples of metaheuristics are evolutionary algorithms, simulated annealing, tabu search, scatter search, memetic algorithms, variable neighborhood search, iterated local search, greedy radomized adaptive search procedures, estimation of distribution algorithms and ant colony opitmization.