Applied Genetic Programming and Machine Learning (Hardcover)

Hitoshi Iba, Yoshihiko Hasegawa, Topon Kumar Paul

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商品描述

What do financial data prediction, day-trading rule development, and bio-marker selection have in common? They are just a few of the tasks that could potentially be resolved with genetic programming and machine learning techniques. Written by leaders in this field, Applied Genetic Programming and Machine Learning delineates the extension of Genetic Programming (GP) for practical applications.

 

Reflecting rapidly developing concepts and emerging paradigms, this book outlines how to use machine learning techniques, make learning operators that efficiently sample a search space, navigate the search process through the design of objective fitness functions, and examine the search performance of the evolutionary system. It provides a methodology for integrating GP and machine learning techniques, establishing a robust evolutionary framework for addressing tasks from areas such as chaotic time-series prediction, system identification, financial forecasting, classification, and data mining.

 

The book provides a starting point for the research of extended GP frameworks with the integration of several machine learning schemes. Drawing on empirical studies taken from fields such as system identification, finanical engineering, and bio-informatics, it demonstrates how the proposed methodology can be useful in practical inductive problem solving.

商品描述(中文翻譯)

金融數據預測、日交易規則開發和生物標記選擇有什麼共同之處?這些都是可能通過遺傳編程和機器學習技術來解決的任務之一。《應用遺傳編程和機器學習》一書由該領域的領導者撰寫,詳細介紹了遺傳編程(GP)在實際應用中的擴展。

反映了快速發展的概念和新興範式,本書概述了如何使用機器學習技術,設計有效地採樣搜索空間的學習運算子,通過設計客觀適應函數來導航搜索過程,並檢查進化系統的搜索性能。它提供了一種將GP和機器學習技術整合起來的方法,建立了一個強大的進化框架,用於解決混沌時間序列預測、系統識別、金融預測、分類和數據挖掘等領域的任務。

本書為研究擴展的GP框架與多個機器學習方案的整合提供了一個起點。通過從系統識別、金融工程和生物信息學等領域的實證研究中提取的案例,它展示了所提出的方法在實際歸納性問題解決中的用途。