NSGA-II: Enhanced Adaptive Niche Genetic Algorithm with Advanced Implementation Strategies

This book presents the second edition of the enhanced adaptive niche genetic algorithm (NSGA-II), which demonstrates substantially faster search capabilities compared to conventional niche genetic algorithms. The implementation features advanced techniques including fast non-dominated sorting for classification of solutions, crowding distance computation for diversity preservation, and elitist selection mechanisms. The resource provides extensive code examples that illustrate practical applications of constrained handling methods, tournament selection operations, and crossover/mutation mechanisms specific to multi-objective optimization problems. These examples help readers thoroughly understand both theoretical concepts and practical implementations of niche genetic algorithms. The target audience includes students and researchers interested in evolutionary computation and multi-objective optimization. Through studying this material, readers will master fundamental concepts of niche genetic algorithms, learn to implement efficient population initialization routines, understand fitness assignment strategies, and apply these techniques to solve real-world engineering problems. Each code segment includes detailed explanations of algorithm parameters, termination criteria, and performance metrics. Overall, this book serves as an excellent educational resource for learning advanced niche genetic algorithms with practical coding implementations.