Essentials of Metaheuristics
by Sean Luke
Number of pages: 233
This is an open set of lecture notes on metaheuristics algorithms, intended for undergraduate students, practitioners, programmers, and other non-experts. It was developed as a series of lecture notes for an undergraduate course. The topics are short and light on examples and theory.
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by Pat Morin - AU Press
Offered as an introduction to the field of data structures and algorithms, the book covers the implementation and analysis of data structures for sequences (lists), queues, priority queues, unordered dictionaries, ordered dictionaries, and graphs.
by Wolfgang Merkle - ESSLLI
The first part of the course gives an introduction to randomized algorithms and to standard techniques for their derandomization. The second part presents applications of the probabilistic method to the construction of logical models.
by D. P. Williamson, D. B. Shmoys - Cambridge University Press
This book shows how to design approximation algorithms: efficient algorithms that find provably near-optimal solutions. It is organized around techniques for designing approximation algorithms, including greedy and local search algorithms.
by Clifford A. Shaffer - Virginia Tech
A comprehensive treatment of fundamental data structures and algorithm analysis with a focus on how to create efficient data structures and algorithms. Aims to help the reader gain an understanding of how to select or design the best data structure.