Essentials of Metaheuristics

“Essentials of Metaheuristics, First Edition” by Sean Luke is available in PDF format for download. This is an open set of lecture notes on metaheuristics algorithms; developed as a series of lecture notes for an undergraduate course.

Book Description

Interested in the Genetic Algorithm? Simulated Annealing? Ant Colony Optimization? Essentials of Metaheuristics covers these and other metaheuristics algorithms, and is intended for undergraduate students, programmers, and non-experts. The book covers a wide range of algorithms, representations, selection and modification operators, and related topics, and includes 70 figures and 133 algorithms great and small.

Table of Contents

• Introduction
• Gradient-based Optimization
• Single-State Methods
• Population Methods
• Representation
• Parallel Methods
• Coevolution
• Multiobjective Optimization
• Combinatorial Optimization
• Optimization by Model Fitting
• Policy Optimization
• Miscellany

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