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商品描述
The goal of this book is to gather in a single document the most relevant concepts related to modern optimization methods, showing how such concepts and methods can be addressed using the open source, multi-platform R tool. Modern optimization methods, also known as metaheuristics, are particularly useful for solving complex problems for which no specialized optimization algorithm has been developed. These methods often yield high quality solutions with a more reasonable use of computational resources (e.g. memory and processing effort). Examples of popular modern methods discussed in this book are: simulated annealing; tabu search; genetic algorithms; differential evolution; and particle swarm optimization. This book is suitable for undergraduate and graduate students in Computer Science, Information Technology, and related areas, as well as data analysts interested in exploring modern optimization methods using R.
商品描述(中文翻譯)
本書的目標是將與現代優化方法相關的最重要概念匯集在一個文件中,展示如何使用開源的多平台 R 工具來處理這些概念和方法。現代優化方法,也稱為元啟發式演算法,特別適用於解決尚未開發專門優化演算法的複雜問題。這些方法通常能夠在更合理的計算資源使用(例如記憶體和處理努力)下產生高品質的解決方案。本書討論的一些流行現代方法包括:模擬退火(simulated annealing)、禁忌搜尋(tabu search)、遺傳演算法(genetic algorithms)、微分演化(differential evolution)和粒子群優化(particle swarm optimization)。本書適合計算機科學、資訊技術及相關領域的本科生和研究生,以及對使用 R 探索現代優化方法感興趣的數據分析師。