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Learning Classifier Systems (LCS) are one of the major families of techniques that apply evolutionary computation to construct programs that automatically learn from experience. Introduction to Learning Classifier Systems, published in the ‘‘SpringerBriefs in Intelligent Systems’’ book series, is a very good textbook on LCS, and will be of interest to readers of Genetic Programming and Evolvable Machines. The exposition of the topic flows naturally, allowing readers to understand each step. The exercises that the authors propose serve as an interesting closing of the initial process of understanding this topic. To this the authors add access to code to be able to experience the concepts introduced. Urbanowicz and Browne use the multiplexer problem as their example to teach LCS. The multiplexer problem may at first seem very simple, but it turns out to have enough complexity to present LCS clearly and deeply, also the theme can be naturally mapped on to other domains. Introduction to Learning Classifier Systems as a textbook, is organized into five chapters. Chapter one introduces us to the topic, starting with the key questions we ask ourselves when we want to understand the topic. What are the basic components of an LCS? What is a rule and how does it differ from a model? What does an LCS model look like and how is it used to make predictions? Chapter 2 presents and analyzes the core concepts that distinguish LCS from other techniques. Here Urbanowicz and Browne also raise questions that are answered in the chapter. What are rules/classifiers? How can they be represented and evaluated? Why do we evolve a population of rules rather than a single rule as a solution? What is the importance of cooperation and competition among classifiers? How does an LCS interact with problems to find and generalize useful patterns? What problem properties should be considered when deciding whether to apply an LCS? What are the general advantages and disadvantages of LCS? How to begin implementing an LCS?

Ryan J. Urbanowicz, and Will N. Browne: Introduction to learning classifier systems