• Title Essentials of Metaheuristics
• Author(s) Sean Luke
• Publisher: Sean Luke (Zeroth Edition, February, 2010)
• eBook 226 pages, 5.34 MB
• Language: English
• ISBN-10: N/A
• ISBN-13: N/A

Book Description

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 author taught at GMU. The chapters are designed to be printable separately if necessary. As it's lecture notes, the topics are short and light on examples and theory. It's best when complementing other texts. With time, I might remedy this.

Metaheuristic is a common but unfortunate name for any stochastic optimization algorithm intended to be the last resort before giving up and using random or brute-force search. Such algorithms are used for problems where you don't know how to find a good solution, but if shown a candidate solution, you can give it a grade. The algorithmic family includes genetic algorithms, hill-climbing, simulated annealing, ant colony optimization, particle swarm optimization, and so on

• Sean Luke is an Associate Professor at Department of Computer Science, George Mason University

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Related Book Categories:

• Algorithms and Data Structures
• Operations Research (OR) and Optimization

• Essentials of Metaheuristics ©2010 (Sean Luke)