Special issue on the 20th anniversary of XCS

This issue is dedicated to Stewart Wilson both in recognition of his achievements and in appreciation for his guidance of the field. Not only has his research transformed the field, but his support, encouragement, and advice have been invaluable to many of us individually and to the community as a whole. Learning Classifier Systems (LCS) are rule-based machine learning algorithms introduced by Holland (see [2, 4–6]) that learn a population of IF-THEN rules that specify ‘‘IF x happens THEN do (or predict) y’’. In 1995 Stewart Wilson, already a leading LCS researcher, published a paper entitled ‘‘Classifier Fitness based on Accuracy’’ [16] that introduced the XCS algorithm. This paper proved a turning point for the field as XCS and its derivatives rapidly became the main focus of LCS research, and continue to be so today. We organised this special issue of Evolutionary Intelligence to mark the 20th anniversary of this landmark paper, and to serve as post-proceedings for IWLCS 2014: the Seventeenth International Workshop on Learning Classifier Systems. The annual IWLCS meetings are the yearly highlight of the LCS calendar and have no doubt contributed to the strong sense of community in the LCS field. For 2015 IWLCS has been rebranded as the more descriptive IWERML: the International Workshop on Evolutionary Rule-based Machine Learning. In 1994, the year before introducing XCS, Wilson introduced ZCS, the ‘‘Zeroth-order Classifier System’’ [15]. Wilson felt the growing complexity of the LCS concept was hindering progress and ZCS was an attempt to strip the LCS down to its essentials while retaining a working system. XCS built on the minimalist ZCS with two radical changes: a switch to accuracy-based fitness and, based on work by Booker [1], the addition of a niche Genetic Algorithm (GA) that strongly favours general rules. This combination was a hit: the niche GA favours general rules but accuracy-based fitness insists strictly that they make good predictions. This combination drives XCS to learn rules that are as general as possible while remaining accurate. Bull’s article in this issue provides much more on the history of LCS prior to and following XCS [2]. The famous ‘‘accuracy-based fitness’’ of XCS needs some explanation. The earlier ZCS featured ‘‘strengthbased fitness’’, in which the fitness of a rule in the GA was derived from its strength, a measure of the amount of reward the rule received. Consequently, fit rules in ZCS are those that receive a lot of reward. In contrast, XCS rules are fit if they make consistently accurate predictions about the reward they receive. This has the counterintuitive consequence that XCS rules that consistently take a bad action can be fit, since they are consistent. However, this poses no problem when it comes time for XCS to choose an action, since that can be done using the magnitude of the reward predicted by each rule. Why does XCS retain rules whose action it does not use? This ‘‘complete map’’ of the state/ & Muhammad Iqbal muhammad.iqbal@ecs.vuw.ac.nz