Genetic Algorithm: Fundamentals and Applications
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In the fields of computer science and operations research, a genetic algorithm (GA) is a metaheuristic that is modeled after the process of natural selection and is a subcategory of evolutionary algorithms (EA), which are a broader category. By relying on biologically inspired operators like mutation, crossover, and selection, genetic algorithms are often employed to develop high-quality solutions to optimization and search problems. This is accomplished through the use of genetic programming. Applications of GA include, but are not limited to, improving the efficiency of decision trees through optimization, deciphering sudoku puzzles, optimizing hyperparameters, drawing causal inferences, and other similar tasks.
How You Will Benefit
(I) Insights, and validations about the following topics:
Chapter 1: Genetic algorithm
Chapter 2: Genetic programming
Chapter 3: Particle filter
Chapter 4: Schema (genetic algorithms)
Chapter 5: Universal Darwinism
Chapter 6: Metaheuristic
Chapter 7: Learning classifier system
Chapter 8: Rule-based machine learning
Chapter 9: Genetic representation
Chapter 10: Fitness function
(II) Answering the public top questions about genetic algorithm.
(III) Real world examples for the usage of genetic algorithm in many fields.
(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of genetic algorithm' 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 genetic algorithm.
