"Search Algorithms and Systems"
"Search Algorithms and Systems" is a comprehensive and meticulously structured exploration of search, spanning theoretical foundations, practical algorithms, and cutting-edge system designs. Beginning with formal models, complexity theory, and the critical dimensions of optimality and computability, the book lays a rigorous groundwork for understanding the diverse landscape of search problems. Successive chapters present an in-depth analysis of classical and heuristic search algorithms, including BFS, DFS, A*, and state-of-the-art quantum and learning-based heuristic methods, all enriched by empirical insights and detailed performance comparisons.
The text progresses into advanced territory with extensive treatments of stochastic, local, and metaheuristic approaches—such as genetic algorithms, swarm intelligence, and hybrid metaheuristics—as well as a robust examination of constraint and combinatorial search via CSPs, SAT solvers, and graph-based strategies. It then pivots to the architecture of real-world search systems, unveiling the intricacies of information retrieval, indexing, distributed and cloud-native search platforms, and scalability challenges, while also addressing essential concerns like security, privacy, and fault tolerance in modern infrastructures.
Notably, the book bridges theoretical innovation and tangible application through dedicated coverage of search in AI and machine learning, including automated planning, adversarial search, reinforcement learning, deep neural models, and AutoML. It culminates with forward-looking perspectives on human-centered, ethical, and explainable search, hardware-accelerated and quantum techniques, and diverse applications from bioinformatics to big data analytics. "Search Algorithms and Systems" thus serves as an authoritative resource for researchers, engineers, and practitioners striving to master the science and engineering of search in the digital age.
