Added example in README

README.md

@@ -198,6 +198,66 @@ puts "We managed #{correct_answers} out of #{test_data.data_pairs.size} total"
 puts "Cnn output: #{cnn.output}"
 ```
 
+Evolutionary optimizer example:
+```crystal
+label = {
+      "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
+      "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
+      "virginica"  => [1.to_f64, 0.to_f64, 0.to_f64],
+    }
+
+    iris = SHAInet::Network.new
+    iris.add_layer(:input, 4, :memory, SHAInet.sigmoid)
+    iris.add_layer(:hidden, 4, :memory, SHAInet.sigmoid)
+    iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)
+    iris.fully_connect
+
+    # Get data from a local file
+    outputs = Array(Array(Float64)).new
+    inputs = Array(Array(Float64)).new
+    CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
+      row_arr = Array(Float64).new
+      row[0..-2].each do |num|
+        row_arr << num.to_f64
+      end
+      inputs << row_arr
+      outputs << label[row[-1]]
+    end
+    data = SHAInet::TrainingData.new(inputs, outputs)
+    data.normalize_min_max
+
+    training_data, test_data = data.split(0.9)
+
+    iris.train_es(
+      data: training_data,
+      pool_size: 50,
+      learning_rate: 0.5,
+      sigma: 0.1,
+      cost_function: :c_ent,
+      epochs: 500,
+      mini_batch_size: 15,
+      error_threshold: 0.00000001,
+      log_each: 100,
+      show_slice: true)
+
+    # Test the trained model
+    correct = 0
+    test_data.data.each do |data_point|
+      result = iris.run(data_point[0], stealth: true)
+      expected = data_point[1]
+      # puts "result: \t#{result.map { |x| x.round(5) }}"
+      # puts "expected: \t#{expected}"
+      error_sum = 0.0
+      result.size.times do |i|
+        error_sum += (result[i] - expected[i]).abs
+      end
+      correct += 1 if error_sum < 0.3
+    end
+    puts "Correct answers: (#{correct} / #{test_data.size})"
+    (correct > 10).should eq(true)
+```
+
+
 ## Development
 
 ### Basic Features