Most symbolic classifiers aim at building sets of rules with good coverage and precision. While this is suitable for most applications, they tend to neglect other desirable properties, such as the ability to induce novel knowledge or to show new points of view of well-established concepts. An approach to overcome these limitations involves using a multi-objective evolutionary algorithm to build knowledge rules with specific properties specified by the user. In this paper, we report a research work that combined evolutionary algorithms and ranking composition methods for multi-objective optimization. In this approach, candidate solutions are built, evaluated and ranked according to their performance in each individual objective. Then rankings are composed into a single ranking which reflects the candidate solutions' ability to solve the multi-objective problem considering all objectives simultaneously. We investigate the behavior of 5 ranking composition methods. These methods are compared and we conclude that all of the studied ranking composition methods provide good balance of objectives. Moreover, for the 11 datasets analyzed, we conclude condorcet is the only method which performs statistically better than other methods.
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