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What Is Metaheuristic
In the fields of computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, tune, or select a heuristic that may provide a sufficiently good solution to an optimization problem or a machine learning problem. This is especially useful in situations where there is incomplete or imperfect information or limited computation capacity. Metaheuristics take a small sample from a much larger pool of potential solutions, which would otherwise be impossible to completely enumerate or otherwise investigate. It's possible that metaheuristics will make relatively few assumptions about the optimization problem that has to be solved, and as a result, they might be applicable to a wide range of issues.
How You Will Benefit
(I) Insights, and validations about the following topics:
Chapter 1: Metaheuristic
Chapter 2: Swarm intelligence
Chapter 3: Genetic algorithm
Chapter 4: Simulated annealing
Chapter 5: Hyper-heuristic
Chapter 6: Genetic programming
Chapter 7: Stochastic optimization
Chapter 8: Meta-optimization
Chapter 9: Workforce modeling
Chapter 10: Matheuristics
(II) Answering the public top questions about metaheuristic.
(III) Real world examples for the usage of metaheuristic in many fields.
(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of metaheuristic' technologies.
Who This Book Is For
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of metaheuristic.
In the fields of computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, tune, or select a heuristic that may provide a sufficiently good solution to an optimization problem or a machine learning problem. This is especially useful in situations where there is incomplete or imperfect information or limited computation capacity. Metaheuristics take a small sample from a much larger pool of potential solutions, which would otherwise be impossible to completely enumerate or otherwise investigate. It's possible that metaheuristics will make relatively few assumptions about the optimization problem that has to be solved, and as a result, they might be applicable to a wide range of issues.
How You Will Benefit
(I) Insights, and validations about the following topics:
Chapter 1: Metaheuristic
Chapter 2: Swarm intelligence
Chapter 3: Genetic algorithm
Chapter 4: Simulated annealing
Chapter 5: Hyper-heuristic
Chapter 6: Genetic programming
Chapter 7: Stochastic optimization
Chapter 8: Meta-optimization
Chapter 9: Workforce modeling
Chapter 10: Matheuristics
(II) Answering the public top questions about metaheuristic.
(III) Real world examples for the usage of metaheuristic in many fields.
(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of metaheuristic' technologies.
Who This Book Is For
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of metaheuristic.
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