Genetic algorithms in Java basics [electronic resource] / Lee Jacobson, Burak Kanber.

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