diff --git a/docs/src/tutorials/advanced_search.ipynb b/docs/src/tutorials/advanced_search.ipynb
index d0ad878..29dba13 100644
--- a/docs/src/tutorials/advanced_search.ipynb
+++ b/docs/src/tutorials/advanced_search.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -115,7 +115,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "There is also another search method called `search_best` which return both the solution and the possible error. The method returns the best program found so far. In this case, we can also see the error (`typemax(Int)`):"
+    "There is another search method called `search_best` which returns both the solution and the possible error. The method returns the best program found so far. In this case, we can also see the error (`typemax(Int)`):"
    ]
   },
   {
@@ -135,11 +135,11 @@
    "source": [
     "## Search methods\n",
     "\n",
-    "We now show examples of using different search procedures, which are initialized by using different enumerators that are passed to the search function.\n",
+    "We now show examples of using different search procedures, which are initialized by passing different enumerators to the search function.\n",
     "\n",
     "### Breadth-First Search\n",
     "\n",
-    "The breadth-first search will first enumerate all possible programs at the same depth before considering a program with a depth of one more. A tree of the grammar is returned with programs ordered in increasing sizes. We can first `collect` the programs that have a `max-depth` of 2 and a `max_size` of infinite (integer maximum value), where the starting symbol is of type `Real`. This function uses a default heuristic 'left-most first', such that the left-most child in the tree is always explored first."
+    "The breadth-first search will first enumerate all possible programs at the same depth before considering programs with a depth of one more. A tree of the grammar is returned with programs ordered in increasing sizes. We can first `collect` the programs that have a `max-depth` of 2 and a `max_size` of infinite (integer maximum value), where the starting symbol is of type `Real`. This function uses a default heuristic 'left-most first', such that the left-most child in the tree is always explored first."
    ]
   },
   {
@@ -159,7 +159,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can test that this function returns the correct functions and all functions. "
+    "We can test that this function returns all and only the correct functions. "
    ]
   },
   {
@@ -267,7 +267,7 @@
     "\n",
     "One of the stochastic search methods that is implemented is Metropolis-Hastings (MH), which samples from a distribution of programs based on the grammar. For more information on MH, see for example [this webpage](https://stephens999.github.io/fiveMinuteStats/MH_intro.html).\n",
     "\n",
-    "The below example uses a simple arithmetic example. You can try running this code block multiple times, which will give different programs, as the search is stochastic. "
+    "The example below uses a simple arithmetic example. You can try running this code block multiple times, which will give different programs, as the search is stochastic. "
    ]
   },
   {
@@ -276,8 +276,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "e = Meta.parse(\"x -> x * x + 4\")\n",
-    "problem, examples = create_problem(eval(e))\n",
+    "e = x -> x * x + 4\n",
+    "problem, examples = create_problem(e)\n",
     "enumerator = get_mh_enumerator(examples, mean_squared_error)\n",
     "program, cost = search_best(grammar, problem, :X, enumerator=enumerator, error_function=mse_error_function, max_depth=3)"
    ]
@@ -288,9 +288,9 @@
    "source": [
     "### Very Large Scale Neighbourhood Search \n",
     "\n",
-    "The second implemented stochastic search method is VLSN, which search for a local optimum in the neighbourhood. For more information, see [this article](https://backend.orbit.dtu.dk/ws/portalfiles/portal/5293785/Pisinger.pdf).\n",
+    "The second implemented stochastic search method is VLSN, which searches for a local optimum in the neighbourhood. For more information, see [this article](https://backend.orbit.dtu.dk/ws/portalfiles/portal/5293785/Pisinger.pdf).\n",
     "\n",
-    "Given the same grammar as before, we can try with some simple examples."
+    "Given the same grammar as before, we can try it with some simple examples."
    ]
   },
   {
@@ -299,9 +299,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "e = Meta.parse(\"x -> 10\")\n",
+    "e = x -> 10\n",
     "max_depth = 2\n",
-    "problem, examples = create_problem(eval(e))\n",
+    "problem, examples = create_problem(e)\n",
     "enumerator = get_vlsn_enumerator(examples, mean_squared_error, max_depth)\n",
     "program, cost = search_best(grammar, problem, :X, enumerator=enumerator, error_function=mse_error_function, max_depth=max_depth)\n"
    ]
@@ -312,9 +312,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "e = Meta.parse(\"x -> x\")\n",
+    "e = x -> x\n",
     "max_depth = 1\n",
-    "problem, examples = create_problem(eval(e))\n",
+    "problem, examples = create_problem(e)\n",
     "enumerator = get_vlsn_enumerator(examples, mean_squared_error, max_depth)\n",
     "program, cost = search_best(grammar, problem, :X, enumerator=enumerator, error_function=mse_error_function, max_depth=max_depth)"
    ]
@@ -325,7 +325,7 @@
    "source": [
     "### Simulated Annealing\n",
     "\n",
-    "The third stochastic search method is called simulated annealing, is another hill-climbing method to find local optima. For more information, see [this page](https://www.cs.cmu.edu/afs/cs.cmu.edu/project/learn-43/lib/photoz/.g/web/glossary/anneal.html).\n",
+    "The third stochastic search method is called simulated annealing. This is another hill-climbing method to find local optima. For more information, see [this page](https://www.cs.cmu.edu/afs/cs.cmu.edu/project/learn-43/lib/photoz/.g/web/glossary/anneal.html).\n",
     "\n",
     "We try the example from earlier, but now we can additionally define the `initial_temperature` of the algorithm, which is 1 by default. Change the value below to see the effect."
    ]
@@ -336,9 +336,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "e = Meta.parse(\"x -> x * x + 4\")\n",
+    "e = x -> x * x + 4\n",
     "initial_temperature = 1\n",
-    "problem, examples = create_problem(eval(e))\n",
+    "problem, examples = create_problem(e)\n",
     "enumerator = get_sa_enumerator(examples, mean_squared_error, initial_temperature)\n",
     "program, cost = search_best(grammar, problem, :X, enumerator=enumerator, error_function=mse_error_function, max_depth=3)        "
    ]
@@ -349,9 +349,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "e = Meta.parse(\"x -> x * x + 4\")\n",
+    "e = x -> x * x + 4\n",
     "initial_temperature = 2\n",
-    "problem, examples = create_problem(eval(e))\n",
+    "problem, examples = create_problem(e)\n",
     "enumerator = get_sa_enumerator(examples, mean_squared_error, initial_temperature)\n",
     "program, cost = @time search_best(grammar, problem, :X, enumerator=enumerator, error_function=mse_error_function, max_depth=3)"
    ]
@@ -373,8 +373,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "e = Meta.parse(\"x -> 3 * x * x + (x + 2)\")\n",
-    "problem, examples = create_problem(eval(e))\n",
+    "e = x -> 3 * x * x + (x + 2)\n",
+    "problem, examples = create_problem(e)\n",
     "enumerator = get_genetic_enumerator(examples, \n",
     "    initial_population_size = 10,\n",
     "    mutation_probability = 0.8,\n",
@@ -559,7 +559,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -600,7 +600,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Julia 1.9.4",
+   "display_name": "Julia 1.9.0",
    "language": "julia",
    "name": "julia-1.9"
   },
@@ -608,7 +608,7 @@
    "file_extension": ".jl",
    "mimetype": "application/julia",
    "name": "julia",
-   "version": "1.9.4"
+   "version": "1.9.0"
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