Parameter Optimization, gives different results?

Can someone point me in the right direction on what I am doing wrong here? I want to get the best learning and momentum parameter values but the results are slightly suprising.

I'm using an optimize grid analyzer, but if I take the best parameter output from the grid (87%) and apply these directly to exactly the same data the result is only 25%. This difference is rather big, and around the same using new runs on the data so it's consistent.

Below is the code I used, so i assume I somehow misconnected things but I can't get a hold on it ...

<?xml version="1.0" encoding="UTF-8"?><process version="7.3.000">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="7.3.000" expanded="true" name="Process">
    <process expanded="true">
      <operator activated="true" class="retrieve" compatibility="7.3.000" expanded="true" height="68" name="Retrieve wordvector" width="90" x="112" y="34">
        <parameter key="repository_entry" value="../SampleData/wordvector"/>
      </operator>
      <operator activated="true" class="optimize_parameters_grid" compatibility="7.3.000" expanded="true" height="103" name="Optimize Parameters (Grid)" width="90" x="313" y="34">
        <list key="parameters">
          <parameter key="Neural Net.learning_rate" value="[0.1;0.9;5;linear]"/>
          <parameter key="Neural Net.momentum" value="[0.1;0.9;5;linear]"/>
        </list>
        <process expanded="true">
          <operator activated="true" class="concurrency:cross_validation" compatibility="7.3.000" expanded="true" height="145" name="Cross Validation" width="90" x="112" y="34">
            <process expanded="true">
              <operator activated="true" class="neural_net" compatibility="7.3.000" expanded="true" height="82" name="Neural Net" width="90" x="179" y="34">
                <list key="hidden_layers"/>
                <parameter key="training_cycles" value="100"/>
                <parameter key="learning_rate" value="0.1"/>
                <parameter key="momentum" value="0.1"/>
              </operator>
              <connect from_port="training set" to_op="Neural Net" to_port="training set"/>
              <connect from_op="Neural Net" from_port="model" to_port="model"/>
              <portSpacing port="source_training set" spacing="0"/>
              <portSpacing port="sink_model" spacing="0"/>
              <portSpacing port="sink_through 1" spacing="0"/>
            </process>
            <process expanded="true">
              <operator activated="true" class="apply_model" compatibility="7.3.000" expanded="true" height="82" name="Apply Model" width="90" x="179" y="85">
                <list key="application_parameters"/>
              </operator>
              <operator activated="true" class="performance_classification" compatibility="7.3.000" expanded="true" height="82" name="Performance" width="90" x="313" y="85">
                <list key="class_weights"/>
              </operator>
              <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="performance 1"/>
              <portSpacing port="source_model" spacing="0"/>
              <portSpacing port="source_test set" spacing="0"/>
              <portSpacing port="source_through 1" spacing="0"/>
              <portSpacing port="sink_test set results" spacing="0"/>
              <portSpacing port="sink_performance 1" spacing="0"/>
              <portSpacing port="sink_performance 2" spacing="0"/>
            </process>
          </operator>
          <operator activated="true" class="log" compatibility="7.3.000" expanded="true" height="82" name="Log" width="90" x="313" y="85">
            <list key="log">
              <parameter key="learning rate" value="operator.Neural Net.parameter.learning_rate"/>
              <parameter key="momentum" value="operator.Neural Net.parameter.momentum"/>
              <parameter key="performance" value="operator.Cross Validation.value.performance 1"/>
            </list>
          </operator>
          <connect from_port="input 1" to_op="Cross Validation" to_port="example set"/>
          <connect from_op="Cross Validation" from_port="performance 1" to_op="Log" to_port="through 1"/>
          <connect from_op="Log" from_port="through 1" to_port="performance"/>
          <portSpacing port="source_input 1" spacing="0"/>
          <portSpacing port="source_input 2" spacing="0"/>
          <portSpacing port="sink_performance" spacing="0"/>
          <portSpacing port="sink_result 1" spacing="0"/>
        </process>
      </operator>
      <connect from_op="Retrieve wordvector" from_port="output" to_op="Optimize Parameters (Grid)" to_port="input 1"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="0"/>
    </process>
  </operator>
</process>