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Genetic Algorithms for Decision Optimization
Abstract
This chapter thoroughly explores the versatile applications of genetic algorithms (GAs) in decision optimization across diverse domains. GAs, inspired by natural selection, adeptly navigate solution spaces, adapting to complex objectives and revealing near-optimal solutions. The exploration encompasses theoretical foundations, customizations for decision optimization, and a variety of GA algorithms demonstrated through real-world case studies, showcasing their adaptability. The GA process iteratively evolves populations, incorporating multiple tailored algorithms that address conflicting objectives and intricate variables. GAs, positioned as optimization keys, redefine decision-making for complex problems where traditional methods fall short. The chapter provides a concise yet comprehensive overview, offering valuable insights into the nuanced applications of GA algorithms in decision optimization.
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