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Genetic algorithms in Java basics /
Genetic Algorithms in Java Basics is a brief introduction to solving problems using genetic algorithms, with working projects and solutions written in the Java programming language. This brief book will guide you step-by-step through various implementations of genetic algorithms and some of their co...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York, NY :
Apress,
[2015]
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| Colección: | Expert's voice in Java.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- At a Glance; Contents; About the Authors; About the Technical Reviewers; Preface; Chapter 1: Introduction; What is Artificial Intelligence?; Biologically Analogies; History of Evolutionary Computation; The Advantage of Evolutionary Computation; Biological Evolution; An Example of Biological Evolution; Basic Terminology; Terms; Search Spaces; Fitness Landscapes; Local Optimums; Parameters; Mutation Rate; Population Size; Crossover Rate; Genetic Representations; Termination; The Search Process; CITATIONS; Chapter 2: Implementation of a Basic Genetic Algorithm.
- Pre-Implementation Pseudo Code for a Basic Genetic Algorithm; About the Code Examples in this Book; Basic Implementation; The Problem ; Parameters ; Initialization ; Evaluation ; Termination Check ; Crossover ; Roulette Wheel Selection; Crossover Methods; Crossover Pseudo Code; Crossover Implementation; Elitism ; Mutation ; Execution ; Summary ; Chapter 3: Robotic Controllers; Introduction; The Problem; Implementation; Before You Start; Encoding; Initialization; Evaluation; Termination Check; Selection Method and Crossover; Tournament Selection; Single Point Crossover.
- Execution Summary; Exercises; Chapter 4: Traveling Salesman; Introduction; The Problem; Implementation; Before You Start; Encoding; Initialization; Evaluation; Termination Check; Crossover; Mutation; Execution; Summary; Exercises; Chapter 5: Class Scheduling; Introduction; The Problem; Implementation; Before You Start; Encoding; Initialization; The Executive Class; Evaluation; Termination; Mutation; Execution; Analysis and Refinement; Exercises; Summary; Chapter 6: Optimization; Adaptive Genetic Algorithms; Implementation; Exercises; Multi-Heuristics.
- Implementation Exercises; Performance Improvements; Fitness Function Design; Parallel Processing; Fitness Value Hashing; Encoding; Mutation and Crossover Methods; Summary; Index.