A new learning technique based on cooperative
    coevolution is proposed for tackling classification problems.
    For each possible outcome of the classification task, a population
    of if-then rules, all having that certain class as the conclusion part, is evolved. Cooperation
    between rules appears in the evaluation stage, when complete
    sets of rules are formed with the purpose of measuring their
    classification accuracy on the training data. In the end of the evolution
    process, a complete set of rules is extracted by selecting a rule from
    each of the final populations. It is then applied to the test data. Some
    interesting results were obtained from experiments conducted on Fisher's iris benchmark problem.