diff --git a/w7/neg.example.RData b/w7/neg.example.RData
new file mode 100644
index 0000000..d38ea87
Binary files /dev/null and b/w7/neg.example.RData differ
diff --git a/w7/pos.example.RData b/w7/pos.example.RData
new file mode 100644
index 0000000..b77c7ef
Binary files /dev/null and b/w7/pos.example.RData differ
diff --git a/w7/w7.Rmd b/w7/w7.Rmd
new file mode 100644
index 0000000..129ac09
--- /dev/null
+++ b/w7/w7.Rmd
@@ -0,0 +1,293 @@
+```{R fsm}
+# Define FSM class
+FSM <- setRefClass("FSM",
+  fields = list(
+    states = "list",
+    transitions = "list",
+    start_state = "character",
+    accept_states = "character",
+    input_alphabet = "character",
+    output_alphabet = "character"
+  ),
+  methods = list(
+    initialize = function(states, transitions, start_state, accept_states) {
+      states <<- states
+      transitions <<- transitions
+      start_state <<- start_state
+      accept_states <<- accept_states
+      input_alphabet <<- letters
+      output_alphabet <<- c("0", "1")
+    },
+    change_output = function(state, new_output) {
+      states[[state]]$output <<- new_output
+    },
+    modify_transition = function(from_state, input, to_state) {
+      transitions[[from_state]][[input]] <<- to_state
+    },
+    add_state = function(state_name, output) {
+      if (length(states) < 10) {
+        states[[state_name]] <<- list(output = output)
+        transitions[[state_name]] <<- list()
+      }
+    },
+    remove_state = function(state_name) {
+      if (length(states) <= 2 || state_name == start_state) {
+        return(FALSE) # Don't remove if it's the start state or if we have 2 or fewer states
+      }
+
+      states[[state_name]] <<- NULL
+      transitions[[state_name]] <<- NULL
+      for (state in names(transitions)) {
+        transitions[[state]] <<- transitions[[state]][names(transitions[[state]]) != state_name]
+      }
+
+      # If we removed an accept state, choose a new one if necessary
+      if (state_name %in% accept_states) {
+        accept_states <<- setdiff(accept_states, state_name)
+        if (length(accept_states) == 0) {
+          accept_states <<- sample(names(states), 1)
+        }
+      }
+
+      # Redirect any transitions that went to the removed state
+      for (state in names(transitions)) {
+        for (input in names(transitions[[state]])) {
+          if (transitions[[state]][[input]] == state_name) {
+            transitions[[state]][[input]] <<- sample(setdiff(names(states), state), 1)
+          }
+        }
+      }
+
+      return(TRUE)
+    },
+    change_start_state = function(new_start_state) {
+      start_state <<- new_start_state
+    },
+    run = function(input_string) {
+      if (nchar(input_string) == 0) {
+        return(states[[start_state]]$output)
+      }
+
+      current_state <- start_state
+      for (char in strsplit(input_string, "")[[1]]) {
+        if (!is.null(transitions[[current_state]][[char]])) {
+          current_state <- transitions[[current_state]][[char]]
+        }
+        # Stay in current state if no transition is defined
+      }
+
+      if (is.null(states[[current_state]]) || is.null(states[[current_state]]$output)) {
+        warning("Invalid final state or missing output")
+        warning(sprintf("Current state: %s", current_state))
+        return("0") # Default output
+      }
+
+      return(states[[current_state]]$output)
+    },
+    copy = function() {
+      FSM$new(
+        states = lapply(states, function(x) list(output = x$output)),
+        transitions = lapply(transitions, function(x) as.list(x)),
+        start_state = start_state,
+        accept_states = accept_states
+      )
+    },
+    check = function() {
+      # Check if start state exists
+      if (!(start_state %in% names(states))) {
+        cat("Error: Start state does not exist\n")
+        return(FALSE)
+      }
+
+      # Check if all accept states exist
+      for (accept_state in accept_states) {
+        if (!(accept_state %in% names(states))) {
+          cat("Error: Accept state", accept_state, "does not exist\n")
+          return(FALSE)
+        }
+      }
+
+      # Check if all transitions are valid
+      for (from_state in names(transitions)) {
+        if (!(from_state %in% names(states))) {
+          cat("Error: Transition from non-existent state", from_state, "\n")
+          return(FALSE)
+        }
+        for (input in names(transitions[[from_state]])) {
+          to_state <- transitions[[from_state]][[input]]
+          if (!(to_state %in% names(states))) {
+            cat("Error: Transition to non-existent state", to_state, "\n")
+            return(FALSE)
+          }
+        }
+      }
+
+      # Check if all states have an output
+      for (state in names(states)) {
+        if (is.null(states[[state]]$output)) {
+          cat("Error: State", state, "has no output\n")
+          return(FALSE)
+        }
+      }
+
+      # cat("FSM is well-formed\n")
+      return(TRUE)
+    }
+  ),
+)
+
+# Create a new FSM
+create_fsm <- function() {
+  num_states <- sample(2:10, 1)
+  state_names <- paste0("S", 1:num_states)
+
+  states <- lapply(state_names, function(s) {
+    list(output = sample(c("0", "1"), 1))
+  })
+  names(states) <- state_names
+
+  transitions <- lapply(state_names, function(s) {
+    num_transitions <- max(1, sample(1:10, 1)) # Ensure at least one transition
+    inputs <- sample(letters, num_transitions, replace = TRUE)
+    to_states <- sample(state_names, num_transitions, replace = TRUE)
+    trans <- setNames(to_states, inputs)
+    return(as.list(trans))
+  })
+  names(transitions) <- state_names
+
+  start_state <- sample(state_names, 1)
+  accept_states <- sample(state_names, 1)
+
+  FSM$new(states, transitions, start_state, accept_states)
+}
+
+# Mutation operation
+mutate_fsm <- function(fsm) {
+  mutation_type <- sample(1:5, 1)
+  new_fsm <- fsm$copy()
+
+  if (mutation_type == 2 && length(new_fsm$states) <= 2) {
+    if (mutation_type == 3 && length(new_fsm$states) >= 10) {
+      mutation_type <- sample(c(1, 4, 5), 1)
+    } else {
+      mutation_type <- sample(c(1, 3, 4, 5), 1)
+    }
+  } else if (mutation_type == 3 && length(new_fsm$states) >= 10) {
+    sample(c(1, 2, 4, 5), 1)
+  }
+
+  cat(mutation_type)
+
+  if (mutation_type == 1) {
+    # Change output signal
+    state <- sample(names(new_fsm$states), 1)
+    new_output <- sample(new_fsm$output_alphabet, 1)
+    new_fsm$change_output(state, new_output)
+  } else if (mutation_type == 2) {
+    # Modify transition
+    from_state <- sample(names(new_fsm$states), 1)
+    input <- sample(new_fsm$input_alphabet, 1)
+    to_state <- sample(names(new_fsm$states), 1)
+    new_fsm$modify_transition(from_state, input, to_state)
+  } else if (mutation_type == 3 && length(new_fsm$states) < 10) {
+    # Add state
+    new_state <- paste0("S", length(new_fsm$states) + 1)
+    new_output <- sample(new_fsm$output_alphabet, 1)
+    new_fsm$add_state(new_state, new_output)
+    # Add at least one transition to/from the new state
+    from_state <- sample(c(names(new_fsm$states), new_state), 1)
+    to_state <- sample(c(names(new_fsm$states), new_state), 1)
+    input <- sample(new_fsm$input_alphabet, 1)
+    new_fsm$modify_transition(from_state, input, to_state)
+  } else if (mutation_type == 4 && length(new_fsm$states) > 2) {
+    # Remove state
+    state_to_remove <- sample(names(new_fsm$states), 1)
+    new_fsm$remove_state(state_to_remove)
+  } else if (mutation_type == 5) {
+    # Change start state
+    new_start_state <- sample(names(new_fsm$states), 1)
+    new_fsm$change_start_state(new_start_state)
+  }
+
+  new_fsm$check()
+
+  return(new_fsm)
+}
+
+# Evaluate FSM fitness
+evaluate_fitness <- function(fsm, pos_examples, neg_examples) {
+  correct <- 0
+  total <- length(pos_examples) + length(neg_examples)
+
+  for (example in pos_examples) {
+    if (fsm$run(example) == "1") correct <- correct + 1
+  }
+  for (example in neg_examples) {
+    if (fsm$run(example) == "0") correct <- correct + 1
+  }
+
+  return(correct / total)
+}
+
+# Evolutionary Algorithm
+evolve_fsm <- function(pos_examples, neg_examples, generations = 100) {
+  population <- replicate(10, create_fsm())
+
+  wlog <- list()
+
+  for (gen in 1:generations) {
+    # Create children through mutation
+    children <- lapply(population, mutate_fsm)
+
+    # Evaluate fitness of all FSMs
+    cat("eval...\n")
+    all_fsms <- c(population, children)
+    fitness_scores <- sapply(all_fsms, evaluate_fitness, pos_examples, neg_examples)
+    cat("max fitness: ")
+    cat(max(fitness_scores))
+    cat("\n")
+
+    # Select the best 10 FSMs for the next generation
+    population <- all_fsms[order(fitness_scores, decreasing = TRUE)[1:10]]
+
+    # Calculate statistics for the current generation
+    state_nums <- sapply(population, function(fsm) length(fsm$states))
+    transition_nums <- sapply(population, function(fsm) sum(sapply(fsm$transitions, function(trans) length(trans))))
+
+    wlog[[gen]] <- list(
+      generation = gen,
+      best_fitness = max(fitness_scores),
+      worst_fitness = min(fitness_scores),
+      average_fitness = mean(fitness_scores),
+      median_fitness = median(fitness_scores),
+      max_state_num = max(state_nums),
+      min_state_num = min(state_nums),
+      avg_state_num = mean(state_nums),
+      median_state_num = median(state_nums),
+      max_transition_num = max(transition_nums),
+      min_transition_num = min(transition_nums),
+      avg_transition_num = mean(transition_nums),
+      median_transition_num = median(transition_nums)
+    )
+
+    cat(sprintf("Generation %d: Best Fitness = %.4f, Avg Fitness = %.4f\n", gen, max(fitness_scores), mean(fitness_scores)))
+    cat(sprintf("               Min Fitness = %.4f, Std Dev = %.4f\n", min(fitness_scores), sd(fitness_scores)))
+    cat(sprintf("               Median Fitness = %.4f\n", median(fitness_scores)))
+    cat(sprintf("               Max States = %d, Min States = %d\n", max(state_nums), min(state_nums)))
+    cat(sprintf("               Avg States = %.2f, Median States = %.1f\n", mean(state_nums), median(state_nums)))
+    cat(sprintf("               Max Transitions = %d, Min Transitions = %d\n", max(transition_nums), min(transition_nums)))
+    cat(sprintf("               Avg Transitions = %.2f, Median Transitions = %.1f\n", mean(transition_nums), median(transition_nums)))
+  }
+
+  # Return the best FSM
+  best_fsm <- population[[which.max(sapply(population, evaluate_fitness, pos_examples, neg_examples))]]
+  return(list(res = best_fsm, wlog = wlog))
+}
+
+# Load example data
+load("pos.example.RData")
+load("neg.example.RData")
+
+# Run the evolutionary algorithm
+best_fsm <- evolve_fsm(pos.examples, neg.examples)
+```
\ No newline at end of file
diff --git a/w7/w7.Rproj b/w7/w7.Rproj
new file mode 100644
index 0000000..8e3c2eb
--- /dev/null
+++ b/w7/w7.Rproj
@@ -0,0 +1,13 @@
+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX