Does Optimize Weights (Evolutionary) support nominal attributes?

Hello

I get an "out of bounds" error when using the Optimize Weights (Evolutionary) operator if the example set contains a nominal attribute. I managed to create an example using generated data to show this. If you delete the attribute. or map it to a single, but pointless, value the process runs fine. Is this a bug and if so can anyone suggest a workaround?

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.0">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" expanded="true" name="Process">
    <process expanded="true" height="512" width="993">
      <operator activated="true" class="generate_data" expanded="true" height="60" name="Generate Data" width="90" x="45" y="75">
        <parameter key="target_function" value="interaction classification"/>
        <parameter key="number_examples" value="1000"/>
        <parameter key="number_of_attributes" value="3"/>
      </operator>
      <operator activated="true" class="nominal_to_numerical" expanded="true" height="94" name="Nominal to Numerical" width="90" x="179" y="75">
        <parameter key="include_special_attributes" value="true"/>
      </operator>
      <operator activated="true" class="generate_attributes" expanded="true" height="76" name="Generate Attributes" width="90" x="313" y="75">
        <list key="function_descriptions">
          <parameter key="integer" value="ceil(rand()*3)"/>
        </list>
      </operator>
      <operator activated="true" class="numerical_to_polynominal" expanded="true" height="76" name="Numerical to Polynominal" width="90" x="447" y="75">
        <parameter key="attribute_filter_type" value="subset"/>
        <parameter key="attributes" value="integer|label"/>
        <parameter key="include_special_attributes" value="true"/>
      </operator>
      <operator activated="true" class="set_role" expanded="true" height="76" name="Set Role" width="90" x="581" y="75">
        <parameter key="name" value="label"/>
        <parameter key="target_role" value="label"/>
      </operator>
      <operator activated="true" class="map" expanded="true" height="76" name="Map" width="90" x="246" y="255">
        <parameter key="attribute_filter_type" value="subset"/>
        <parameter key="attributes" value="integer|label"/>
        <parameter key="include_special_attributes" value="true"/>
        <list key="value_mappings">
          <parameter key="1" value="one"/>
          <parameter key="2" value="two"/>
          <parameter key="3" value="three"/>
          <parameter key="0" value="zero"/>
        </list>
      </operator>
      <operator activated="true" class="optimize_weights_evolutionary" expanded="true" height="94" name="Optimize Weights (Evolutionary)" width="90" x="447" y="255">
        <process expanded="true">
          <operator activated="true" class="x_validation" expanded="true" height="112" name="Validation" width="90" x="179" y="210">
            <process expanded="true" height="505" width="447">
              <operator activated="true" class="naive_bayes_kernel" expanded="true" height="76" name="Naive Bayes (Kernel)" width="90" x="178" y="30"/>
              <connect from_port="training" to_op="Naive Bayes (Kernel)" to_port="training set"/>
              <connect from_op="Naive Bayes (Kernel)" from_port="model" to_port="model"/>
              <portSpacing port="source_training" spacing="0"/>
              <portSpacing port="sink_model" spacing="0"/>
              <portSpacing port="sink_through 1" spacing="0"/>
            </process>
            <process expanded="true" height="505" width="447">
              <operator activated="true" class="apply_model" expanded="true" height="76" name="Apply Model" width="90" x="88" y="25">
                <list key="application_parameters"/>
              </operator>
              <operator activated="true" class="performance" expanded="true" height="76" name="Performance" width="90" x="246" y="30"/>
              <connect from_port="model" to_op="Apply Model" to_port="model"/>
              <connect from_port="test set" to_op="Apply Model" to_port="unlabelled data"/>
              <connect from_op="Apply Model" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
              <connect from_op="Performance" from_port="performance" to_port="averagable 1"/>
              <portSpacing port="source_model" spacing="0"/>
              <portSpacing port="source_test set" spacing="0"/>
              <portSpacing port="source_through 1" spacing="0"/>
              <portSpacing port="sink_averagable 1" spacing="0"/>
              <portSpacing port="sink_averagable 2" spacing="0"/>
            </process>
          </operator>
          <connect from_port="example set" to_op="Validation" to_port="training"/>
          <connect from_op="Validation" from_port="averagable 1" to_port="performance"/>
          <portSpacing port="source_example set" spacing="0"/>
          <portSpacing port="source_through 1" spacing="0"/>
          <portSpacing port="sink_performance" spacing="0"/>
        </process>
      </operator>
      <connect from_op="Generate Data" from_port="output" to_op="Nominal to Numerical" to_port="example set input"/>
      <connect from_op="Nominal to Numerical" from_port="example set output" to_op="Generate Attributes" to_port="example set input"/>
      <connect from_op="Generate Attributes" from_port="example set output" to_op="Numerical to Polynominal" to_port="example set input"/>
      <connect from_op="Numerical to Polynominal" from_port="example set output" to_op="Set Role" to_port="example set input"/>
      <connect from_op="Set Role" from_port="example set output" to_op="Map" to_port="example set input"/>
      <connect from_op="Map" from_port="example set output" to_op="Optimize Weights (Evolutionary)" to_port="example set in"/>
      <connect from_op="Optimize Weights (Evolutionary)" from_port="example set out" to_port="result 3"/>
      <connect from_op="Optimize Weights (Evolutionary)" from_port="weights" to_port="result 2"/>
      <connect from_op="Optimize Weights (Evolutionary)" from_port="performance" to_port="result 1"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="0"/>
      <portSpacing port="sink_result 2" spacing="0"/>
    </process>
  </operator>
</process>

regards,

Andrew