Category Archives: Books

The book Multi-objective Machine Learning edited by Yaochu Jin contains several chapters on the usage of LCS and GBML for multi-objective machine learning. Among other topics it includes the usage of multi-objective optimization to evolve accurate and compact rule sets … Continue reading →

This book by Martin Butz offers a comprehensive introduction to learning classifier systems (LCS) – or more generally, rule-based evolutionary online learning systems. LCSs learn interactively – much like a neural network – but with an increased adaptivity and flexibility. … Continue reading →

The book offers a comprehensive introduction to learning classifier systems (LCS) – or more generally, rule-based evolutionary online learning systems. LCSs learn interactively – much like a neural network – but with an increased adaptivity and flexibility. This book provides … Continue reading → Continue reading →

Book Description Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithms provides a framework for the design of competent optimization techniques by combining advanced evolutionary algorithms with state-of-the-art machine learning techniques. The primary focus of the book is … Continue reading → Continue reading →

This carefully edited book by Ashish Ghosh and Lakhmi C. Jainreflects and advances the state of the art in the area of Data Mining and Knowledge Discovery with Evolutionary Algorithms. It emphasizes the utility of different evolutionary computing tools to … Continue reading →

This carefully edited book by Larry Bull brings together a fascinating selection of applications of Learning Classifier Systems (LCS). The book demonstrates the utility of this machine learning technique in recent real-world applications in such domains as data mining, modelling … Continue reading →

From the author:Presents a number of traditional and nontraditional (genetic algorithms and simulated annealing) optimization techniques in an easy-to-understand step-by-step format. Algorithms are supported with numerical examples and computer codes. … Continue reading →

This book constitutes the refereed proceedings of the 5th International Workshop on Learning Classifier Systems, IWLCS 2003, held in Granada, Spain in September 2003 in conjunction with PPSN VII. The 10 revised full papers presented together with a comprehensive bibliography … Continue reading →

Machine learning promises both to create machine intelligence and to shed light on natural intelligence. A fundamental issue for either endevour is that of credit assignment, which we can pose as follows: how can we credit individual components of a … Continue reading →