{ "item_title" : "A Novel Meta-Evolutionary Algorithm", "item_author" : [" Pisut Pongchairerks "], "item_description" : "For over fifteen years, the author has recognized the strong influence of parameter settings on metaheuristic performance, beginning with his doctoral research on Particle Swarm Optimization. This work led to the development of UPLA, a novel meta-evolutionary algorithm designed to automatically evolve parameter values within two-level and multilevel optimization frameworks. With its distinctive evolutionary process, UPLA has delivered strong results across diverse scheduling problems and has outperformed several established algorithms. This book presents the foundations, methodology, and applications of this innovative approach. It offers clear explanations, illustrative examples, and chapter-end exercises, making it suitable for self-study or classroom use. It serves as a comprehensive resource for graduate students and researchers in metaheuristics, optimization, and scheduling. As UPLA continues to advance, many open questions remain. This book provides guidance for future research and practical applications, inviting readers to contribute to the next stage of its development. ", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/1/03/647/780/1036477800_b.jpg", "price_data" : { "retail_price" : "110.95", "online_price" : "110.95", "our_price" : "110.95", "club_price" : "110.95", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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