Extendecl Classifier Systems, or XCS, are a soft-computing approach to machine learning in rule-based Systems. While XCS has been shown effective in learnirig accurate, compact and complete mappnigs of an environmenťs payoff landscape, it can require significant resources to do so. This paper presents four modifications that allow XCS to achieve high performance even in highly size-constrained populations. By modifying (1) the genetic algorithm trigger function, (2) the classifier deletion-selection mechanism, (3) the genetic algorithm selection function, and (4) the frequency of classifier parameter updates, the modified system uses the available population resources more efficiently. Ex{)erimental results demonstrate the irnprovement in performance achievcd with the proposed modifications in both the single-step 6-Multiplexer problem and the niulti-step Woods-2 problem.