diff --git a/.gitignore b/.gitignore
index 1753c21..c6dc6af 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,6 +7,8 @@ __pycache__/
 *.py[cod]
 *$py.class
 
+htmls/*.ipynb
+
 # C extensions
 *.so
 
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 30352e0..9a26ab1 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,6 +9,27 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Released]
 
+## [1.5.0] - 2025-11-21
+
+### Added
+- **Reproducibility support**: Added `seed` parameter to Bayesian statistical tests (`bayesian_sign_test`, `bayesian_signed_rank_test`) for deterministic results
+- **Reproducibility support**: Added `seed` parameter to `HistoPlot` class for consistent jitter in histogram generation
+- **Multi-platform CI**: Comprehensive GitHub Actions workflow testing on Windows, Linux (Ubuntu), and macOS
+- **Smoke tests**: Added automated smoke tests for CLI and API functionality across all platforms
+- **Environment files**: Added `requirements.txt`, `requirements-dev.txt`, and `environment.yml` for broader compatibility
+- **Headless mode documentation**: Complete documentation and examples for running SAES without display (CI/CD, servers)
+- **Headless mode examples**: Python and shell script examples for automated workflows (`examples/headless_mode_example.py`, `examples/headless_cli_example.sh`, `examples/headless_cli_example.bat`)
+- **New test suite**: Added `test_bayesian_seed.py` specifically for verifying seed reproducibility
+- **Documentation**: New reproducibility documentation page (`docs/usage/reproducibility.rst`) with best practices
+
+### Changed
+- Updated `test_bayesian.py` to demonstrate both seed parameter usage and backward compatibility
+- Enhanced README with sections on reproducibility and headless mode
+- Improved documentation structure to include reproducibility guidance
+
+### Fixed
+- Ensured all random operations can be made deterministic for reproducible research
+
 ## [1.4.0] - 2025-11-15
 
 ### Added
diff --git a/README.md b/README.md
index 0f04417..49cd6fa 100644
--- a/README.md
+++ b/README.md
@@ -124,6 +124,51 @@ source venv/bin/activate  # On Windows: venv\Scripts\activate
 pip install -e ".[dev]"
 ```
 
+### Using Environment Files
+
+For broader compatibility, environment files are provided:
+
+```sh
+# Using pip with requirements.txt
+pip install -r requirements.txt
+
+# Using conda with environment.yml
+conda env create -f environment.yml
+conda activate saes
+```
+
+## 🔄 Reproducibility
+
+SAES supports **deterministic seeds** for reproducible research:
+
+```python
+from SAES.statistical_tests.bayesian import bayesian_sign_test
+from SAES.plots.histoplot import HistoPlot
+
+# Bayesian tests with seed for reproducibility
+result, _ = bayesian_sign_test(data, sample_size=5000, seed=42)
+
+# Histogram plots with consistent jitter
+histoplot = HistoPlot(data, metrics, "Accuracy", seed=42)
+```
+
+See the [reproducibility documentation](https://jMetal.github.io/SAES/usage/reproducibility.html) for details.
+
+## 💻 Headless Mode
+
+SAES can run in headless mode (without display) for automated workflows, CI/CD pipelines, and server environments:
+
+```bash
+# Set matplotlib backend
+export MPLBACKEND=Agg
+
+# Run SAES commands
+python -m SAES -ls -ds data.csv -ms metrics.csv -m HV -s friedman -op results.tex
+python -m SAES -bp -ds data.csv -ms metrics.csv -m HV -i Problem1 -op boxplot.png
+```
+
+See `examples/headless_mode_example.py` for a complete Python example or `examples/headless_cli_example.sh` for CLI usage.
+
 ## 🤝 Contributors
 
 - [![GitHub](https://img.shields.io/badge/GitHub-100000?style=flat&logo=github&logoColor=white)](https://github.com/rorro6787) **Emilio Rodrigo Carreira Villalta**
diff --git a/SAES/statistical_tests/bayesian.py b/SAES/statistical_tests/bayesian.py
index d13df96..0933b61 100644
--- a/SAES/statistical_tests/bayesian.py
+++ b/SAES/statistical_tests/bayesian.py
@@ -7,7 +7,8 @@ def bayesian_sign_test(data: pd.DataFrame,
                        rope_limits=[-0.01, 0.01], 
                        prior_strength=0.5, 
                        prior_place="rope", 
-                       sample_size=5000) -> tuple:
+                       sample_size=5000,
+                       seed=None) -> tuple:
     """
     Performs the Bayesian sign test to compare the performance of two algorithms across multiple instances.
     The Bayesian sign test is a non-parametric statistical test used to compare the performance of two algorithms on multiple instances. The null hypothesis is that the algorithms perform equivalently, which implies their average ranks are equal.
@@ -40,6 +41,9 @@ def bayesian_sign_test(data: pd.DataFrame,
         sample_size (int):
             Total number of random_search samples generated. Default is 5000.
         
+        seed (int, optional):
+            Random seed for reproducibility. Default is None (non-deterministic).
+        
     Returns:
         tuple: A tuple containing the posterior probabilities and the samples drawn from the Dirichlet process. List of posterior probabilities:
             - Pr(algorith_1 < algorithm_2)
@@ -62,6 +66,10 @@ def bayesian_sign_test(data: pd.DataFrame,
     else:
         raise ValueError("Initialization ERROR. Incorrect number of dimensions for axis 1")
 
+    # Set random seed for reproducibility
+    if seed is not None:
+        np.random.seed(seed)
+
     # Compute the differences
     Z = sample1 - sample2
 
@@ -93,7 +101,8 @@ def bayesian_signed_rank_test(data,
                               rope_limits=[-0.01, 0.01], 
                               prior_strength=1.0, 
                               prior_place="rope", 
-                              sample_size=1000) -> tuple:
+                              sample_size=1000,
+                              seed=None) -> tuple:
     """
     Performs the Bayesian version of the signed rank test to compare the performance of two algorithms across multiple instances.
     The Bayesian sign test is a non-parametric statistical test used to compare the performance of two algorithms on multiple instances. The null hypothesis is that the algorithms perform equivalently, which implies their average ranks are equal.
@@ -126,6 +135,9 @@ def bayesian_signed_rank_test(data,
         sample_size (int):
             Total number of random_search samples generated. Default is 5000.
         
+        seed (int, optional):
+            Random seed for reproducibility. Default is None (non-deterministic).
+        
     Returns:
         tuple: A tuple containing the posterior probabilities and the samples drawn from the Dirichlet process. List of posterior probabilities:
             - Pr(algorith_1 < algorithm_2)
@@ -153,6 +165,10 @@ def weights(n, s):
     else:
         raise ValueError("Initialization ERROR. Incorrect number of dimensions for axis 1")
 
+    # Set random seed for reproducibility
+    if seed is not None:
+        np.random.seed(seed)
+
     # Compute the differences
     Z = sample1 - sample2
     Z0 = [-float("Inf"), 0.0, float("Inf")][["left", "rope", "right"].index(prior_place)]
diff --git a/SOFTWARE_X_COMPLIANCE.md b/SOFTWARE_X_COMPLIANCE.md
new file mode 100644
index 0000000..3871b19
--- /dev/null
+++ b/SOFTWARE_X_COMPLIANCE.md
@@ -0,0 +1,94 @@
+# Software X Compliance
+
+SAES meets all Software X publication requirements.
+
+## 1. Deterministic Seeds ✅
+
+Bayesian tests support `seed` parameter for reproducibility:
+
+```python
+from SAES.statistical_tests.bayesian import bayesian_sign_test
+result, _ = bayesian_sign_test(data, sample_size=1000, seed=42)
+```
+
+## 2. Multi-Platform CI ✅
+
+`.github/workflows/multi-platform-test.yml` tests on:
+- Ubuntu, Windows, macOS
+- Python 3.10, 3.11, 3.12
+
+## 3. Smoke Tests ✅
+
+Run comprehensive smoke tests (10 tests) - fully automated:
+
+```bash
+chmod +x examples/smoke_test.sh
+./examples/smoke_test.sh
+```
+
+The script automatically:
+- Creates virtual environment if needed
+- Installs dependencies
+- Runs all tests in headless mode
+
+**Tests cover:**
+- **LaTeX tables** (4): Mean/Median, Friedman, Wilcoxon pivot, Wilcoxon pairwise
+- **Plots** (3): Boxplot single, Boxplot grid, Critical distance
+- **Statistical APIs** (3): Bayesian tests with seeds, Plot classes
+
+## 4. Environment Files ✅
+
+Multiple installation options:
+
+```bash
+# Option 1: Requirements file
+pip install -r requirements.txt
+
+# Option 2: Conda
+conda env create -f environment.yml
+conda activate saes
+
+# Option 3: Auto-install (smoke test does this)
+./examples/smoke_test.sh
+```
+
+Files provided:
+- `requirements.txt` - Core dependencies
+- `requirements-dev.txt` - Development dependencies  
+- `environment.yml` - Conda environment
+
+## 5. Headless Mode ✅
+
+For CI/CD and server environments:
+
+```bash
+export MPLBACKEND=Agg
+python -m SAES -ls -ds data.csv -ms metrics.csv -m HV -s friedman -op output.tex
+```
+
+The smoke test script runs in headless mode by default.
+
+## Quick Start
+
+```bash
+# Clone and test
+git clone https://github.com/jMetal/SAES.git
+cd SAES
+chmod +x examples/smoke_test.sh
+./examples/smoke_test.sh
+```
+
+## Verification
+
+```bash
+# Smoke tests (automated setup)
+./examples/smoke_test.sh
+
+# Unit tests
+python -m unittest discover tests
+```
+
+## Branch
+
+Feature branch: `feature/software-x-requirements`  
+Ready for merge (not merged yet, as requested)
diff --git a/docs/usage/reproducibility.rst b/docs/usage/reproducibility.rst
new file mode 100644
index 0000000..e14220f
--- /dev/null
+++ b/docs/usage/reproducibility.rst
@@ -0,0 +1,138 @@
+Reproducibility and Seeds
+=========================
+
+SAES supports deterministic behavior for reproducible research through random seed control.
+
+Why Reproducibility Matters
+---------------------------
+
+When analyzing stochastic algorithms, reproducibility is crucial for:
+
+- **Research validation**: Others can verify your results
+- **Debugging**: Consistent results make it easier to identify issues
+- **Comparisons**: Fair comparison requires consistent conditions
+- **Publication**: Many journals and conferences require reproducible results
+
+Functions with Random Seeds
+---------------------------
+
+The following SAES functions support deterministic execution via the ``seed`` parameter:
+
+Bayesian Statistical Tests
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Both Bayesian tests support the ``seed`` parameter for reproducibility:
+
+.. code-block:: python
+
+    from SAES.statistical_tests.bayesian import bayesian_sign_test, bayesian_signed_rank_test
+    import pandas as pd
+
+    data = pd.DataFrame({
+        'Algorithm_A': [0.9, 0.85, 0.95, 0.9, 0.92],
+        'Algorithm_B': [0.5, 0.6, 0.55, 0.58, 0.52]
+    })
+
+    # Deterministic results with seed
+    result1, _ = bayesian_sign_test(data, sample_size=5000, seed=42)
+    result2, _ = bayesian_sign_test(data, sample_size=5000, seed=42)
+    # result1 and result2 will be identical
+
+    # Same for signed rank test
+    result3, _ = bayesian_signed_rank_test(data, sample_size=1000, seed=123)
+
+Histogram Plots
+~~~~~~~~~~~~~~
+
+The HistoPlot class supports seeding for consistent jitter when handling identical values:
+
+.. code-block:: python
+
+    from SAES.plots.histoplot import HistoPlot
+    import pandas as pd
+
+    data = pd.read_csv("results.csv")
+    metrics = pd.read_csv("metrics.csv")
+
+    # Create histoplot with reproducible jitter
+    histoplot = HistoPlot(data, metrics, "Accuracy", seed=42)
+    histoplot.save_instance("Problem1", "output.png")
+
+Best Practices
+-------------
+
+1. **Always use seeds for published research**: Set explicit seeds for all random operations
+2. **Document your seeds**: Include seed values in your research papers and code
+3. **Use different seeds for different experiments**: Avoid accidentally reusing the same random sequence
+4. **Version control**: Include seed values in your version-controlled analysis scripts
+
+Example: Complete Reproducible Workflow
+---------------------------------------
+
+.. code-block:: python
+
+    from SAES.statistical_tests.bayesian import bayesian_sign_test, bayesian_signed_rank_test
+    from SAES.plots.histoplot import HistoPlot
+    import pandas as pd
+
+    # Load data
+    data = pd.read_csv("algorithm_results.csv")
+    metrics = pd.read_csv("metrics.csv")
+
+    # Reproducible Bayesian analysis
+    SEED = 42
+    algorithm_a = data[data['Algorithm'] == 'A']['MetricValue']
+    algorithm_b = data[data['Algorithm'] == 'B']['MetricValue']
+    
+    comparison_data = pd.DataFrame({
+        'Algorithm_A': algorithm_a.values,
+        'Algorithm_B': algorithm_b.values
+    })
+
+    # Run Bayesian test with seed
+    result, samples = bayesian_sign_test(
+        comparison_data, 
+        sample_size=5000, 
+        seed=SEED
+    )
+
+    print(f"P(A < B): {result[0]:.4f}")
+    print(f"P(A ≈ B): {result[1]:.4f}")
+    print(f"P(A > B): {result[2]:.4f}")
+
+    # Create reproducible visualization
+    histoplot = HistoPlot(data, metrics, "Accuracy", seed=SEED)
+    histoplot.save_all_instances("comparison.png")
+
+Headless Mode for Automated Workflows
+-------------------------------------
+
+SAES can be run in headless mode (without display) for automated pipelines and CI/CD:
+
+.. code-block:: bash
+
+    # Set matplotlib to use non-interactive backend
+    export MPLBACKEND=Agg
+
+    # Run SAES commands
+    python -m SAES -ls -ds data.csv -ms metrics.csv -m HV -s friedman -op results.tex
+    python -m SAES -bp -ds data.csv -ms metrics.csv -m HV -i Problem1 -op boxplot.png
+    python -m SAES -cdp -ds data.csv -ms metrics.csv -m HV -op cdplot.png
+
+For Python scripts in headless environments:
+
+.. code-block:: python
+
+    import matplotlib
+    matplotlib.use('Agg')  # Must be called before importing pyplot
+    
+    from SAES.plots.boxplot import Boxplot
+    import pandas as pd
+
+    # Your analysis code here
+    data = pd.read_csv("results.csv")
+    metrics = pd.read_csv("metrics.csv")
+    
+    boxplot = Boxplot(data, metrics, "Accuracy")
+    boxplot.save_instance("Problem1", "output.png")
+
diff --git a/docs/usage/usage.rst b/docs/usage/usage.rst
index b3f5f40..eec7b98 100644
--- a/docs/usage/usage.rst
+++ b/docs/usage/usage.rst
@@ -14,3 +14,4 @@ This section provides a brief overview of the three different features that this
    html
    bayesian
    violin
+   reproducibility
diff --git a/environment.yml b/environment.yml
new file mode 100644
index 0000000..6e40a18
--- /dev/null
+++ b/environment.yml
@@ -0,0 +1,18 @@
+name: saes
+channels:
+  - conda-forge
+  - defaults
+dependencies:
+  - python>=3.10
+  - pip
+  - pip:
+    - python-dotenv==1.0.1
+    - numpy<2
+    - matplotlib==3.9.2
+    - Jinja2==3.1.6
+    - scikit-posthocs==0.10.0
+    - scipy>=1.7.0
+    - pandas>=1.3.0
+    - seaborn>=0.11.0
+    - papermill==2.6.0
+
diff --git a/examples/smoke_test.sh b/examples/smoke_test.sh
new file mode 100755
index 0000000..457828c
--- /dev/null
+++ b/examples/smoke_test.sh
@@ -0,0 +1,104 @@
+#!/bin/bash
+# SAES Comprehensive Smoke Tests
+# Self-contained: Sets up environment and runs all tests
+
+set -e
+
+echo "=== SAES Smoke Tests ==="
+echo ""
+
+# Setup virtual environment if needed
+if [ ! -d ".venv" ]; then
+    echo "Creating virtual environment..."
+    python3 -m venv .venv
+    echo "✓ Virtual environment created"
+fi
+
+# Activate virtual environment
+echo "Activating virtual environment..."
+source .venv/bin/activate
+
+# Install dependencies if needed
+if ! python -c "import SAES" 2>/dev/null; then
+    echo "Installing SAES..."
+    pip install -q -e ".[test]"
+    echo "✓ SAES installed"
+else
+    echo "✓ SAES already installed"
+fi
+
+# Set headless mode
+export MPLBACKEND=Agg
+mkdir -p /tmp/saes_smoke_tests
+
+echo ""
+echo "Running tests..."
+echo ""
+
+DATA="tests/test_data/swarmIntelligence.csv"
+METRICS="tests/test_data/multiobjectiveMetrics.csv"
+
+# LaTeX Tables (4 types)
+echo "[1/10] LaTeX: Mean/Median table"
+python -m SAES -ls -ds $DATA -ms $METRICS -m HV -s mean_median -op /tmp/saes_smoke_tests/mean_median.tex
+
+echo "[2/10] LaTeX: Friedman test table"
+python -m SAES -ls -ds $DATA -ms $METRICS -m HV -s friedman -op /tmp/saes_smoke_tests/friedman.tex
+
+echo "[3/10] LaTeX: Wilcoxon pivot table"
+python -m SAES -ls -ds $DATA -ms $METRICS -m HV -s wilcoxon_pivot -op /tmp/saes_smoke_tests/wilcoxon_pivot.tex
+
+echo "[4/10] LaTeX: Wilcoxon pairwise table"
+python -m SAES -ls -ds $DATA -ms $METRICS -m HV -s wilcoxon -op /tmp/saes_smoke_tests/wilcoxon.tex
+
+# Plots (3 types)
+echo "[5/10] Plot: Boxplot (single instance)"
+python -m SAES -bp -ds $DATA -ms $METRICS -m HV -i DTLZ1 -op /tmp/saes_smoke_tests/boxplot.png
+
+echo "[6/10] Plot: Boxplot (all instances grid)"
+python -m SAES -bp -ds $DATA -ms $METRICS -m HV -g -op /tmp/saes_smoke_tests/boxplot_grid.png
+
+echo "[7/10] Plot: Critical distance plot"
+python -m SAES -cdp -ds $DATA -ms $METRICS -m HV -op /tmp/saes_smoke_tests/cdplot.png
+
+# Statistical Tests API (3 tests)
+echo "[8/10] API: Bayesian sign test (with seed)"
+python -c "
+from SAES.statistical_tests.bayesian import bayesian_sign_test
+import pandas as pd, numpy as np
+data = pd.DataFrame({'A': [0.9,0.85,0.95,0.9,0.92], 'B': [0.5,0.6,0.55,0.58,0.52]})
+r1, _ = bayesian_sign_test(data, sample_size=1000, seed=42)
+r2, _ = bayesian_sign_test(data, sample_size=1000, seed=42)
+np.testing.assert_array_almost_equal(r1, r2, decimal=10)
+"
+
+echo "[9/10] API: Bayesian signed rank test (with seed)"
+python -c "
+from SAES.statistical_tests.bayesian import bayesian_signed_rank_test
+import pandas as pd, numpy as np
+data = pd.DataFrame({'A': [0.9,0.85,0.95,0.9,0.92], 'B': [0.5,0.6,0.55,0.58,0.52]})
+r1, _ = bayesian_signed_rank_test(data, sample_size=500, seed=123)
+r2, _ = bayesian_signed_rank_test(data, sample_size=500, seed=123)
+np.testing.assert_array_almost_equal(r1, r2, decimal=10)
+"
+
+echo "[10/10] API: Plot classes initialization"
+python -c "
+from SAES.plots.histoplot import HistoPlot
+from SAES.plots.violin import Violin
+from SAES.plots.boxplot import Boxplot
+from SAES.plots.cdplot import CDplot
+import pandas as pd
+data = pd.read_csv('$DATA')
+metrics = pd.read_csv('$METRICS')
+histoplot = HistoPlot(data, metrics, 'HV')
+violin = Violin(data, metrics, 'HV')
+boxplot = Boxplot(data, metrics, 'HV')
+cdplot = CDplot(data, metrics, 'HV')
+"
+
+echo ""
+echo "✓ All 10 smoke tests passed!"
+echo "  Tests cover: LaTeX tables (4), Plots (3), Statistical APIs (3)"
+echo "  Output directory: /tmp/saes_smoke_tests/"
+ls -lh /tmp/saes_smoke_tests/ 2>/dev/null | tail -n +2 | wc -l | xargs echo "  Files created:"
diff --git a/requirements-dev.txt b/requirements-dev.txt
new file mode 100644
index 0000000..a89544e
--- /dev/null
+++ b/requirements-dev.txt
@@ -0,0 +1,25 @@
+# Development dependencies for SAES
+# Install with: pip install -r requirements.txt -r requirements-dev.txt
+
+# Testing
+coverage==7.6.8
+pytest>=7.0.0
+pytest-cov>=3.0.0
+Pillow>=9.0.0
+
+# Code quality
+black>=22.0.0
+flake8>=4.0.0
+mypy>=0.950
+isort>=5.10.0
+pre-commit>=3.0.0
+
+# Documentation
+sphinx==8.1.3
+sphinx_rtd_theme==3.0.2
+
+# HTML generation
+ipython<=8.30.0
+nbconvert==7.16.6
+ipykernel<=6.29.5
+
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..6960468
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,14 @@
+# Core dependencies for SAES
+# Generated from pyproject.toml for broader compatibility
+
+# Core runtime dependencies
+python-dotenv==1.0.1
+numpy<2
+matplotlib==3.9.2
+Jinja2==3.1.6
+scikit-posthocs==0.10.0
+scipy>=1.7.0
+pandas>=1.3.0
+seaborn>=0.11.0
+papermill==2.6.0
+
diff --git a/tests/htmls/bayesian.ipynb b/tests/htmls/bayesian.ipynb
index 3bef08a..b0c77d6 100644
--- a/tests/htmls/bayesian.ipynb
+++ b/tests/htmls/bayesian.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ebd39d2e",
+   "id": "db9d11d7",
    "metadata": {
     "papermill": {
      "duration": null,
@@ -28,7 +28,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a37ca2f0",
+   "id": "7d460180",
    "metadata": {
     "papermill": {
      "duration": null,
@@ -47,7 +47,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1ca39f1b",
+   "id": "d8e756db",
    "metadata": {
     "papermill": {
      "duration": null,
@@ -72,7 +72,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "aac610b3",
+   "id": "4af90d53",
    "metadata": {
     "papermill": {
      "duration": null,
@@ -93,7 +93,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e578e373",
+   "id": "b441bd14",
    "metadata": {
     "papermill": {
      "duration": null,
@@ -111,7 +111,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "46d636d1",
+   "id": "87095eb4",
    "metadata": {
     "papermill": {
      "duration": null,
@@ -129,7 +129,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "803f7afc",
+   "id": "380a838a",
    "metadata": {
     "papermill": {
      "duration": null,
@@ -147,7 +147,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ba6bfa6e",
+   "id": "b9edb5cb",
    "metadata": {
     "papermill": {
      "duration": null,
@@ -165,7 +165,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0d7c5f7b",
+   "id": "4fb9404f",
    "metadata": {
     "papermill": {
      "duration": null,
@@ -202,8 +202,8 @@
   },
   "papermill": {
    "default_parameters": {},
-   "duration": 0.022066,
-   "end_time": "2025-11-08T18:27:09.819556",
+   "duration": 0.026258,
+   "end_time": "2025-11-21T17:51:43.171606",
    "environment_variables": {},
    "exception": null,
    "input_path": "/Users/rorro6787/Desktop/SAES/SAES/html/notebooks/bayesian_posterior.ipynb",
@@ -214,7 +214,7 @@
     "metrics": "tests/test_data/multiobjectiveMetrics.csv",
     "pivot": "NSGAII"
    },
-   "start_time": "2025-11-08T18:27:09.797490",
+   "start_time": "2025-11-21T17:51:43.145348",
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diff --git a/tests/htmls/fronts2D.ipynb b/tests/htmls/fronts2D.ipynb
index bf6137f..165250e 100644
--- a/tests/htmls/fronts2D.ipynb
+++ b/tests/htmls/fronts2D.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "957c9e95",
+   "id": "882c6358",
    "metadata": {
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@@ -29,7 +29,7 @@
   {
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    "execution_count": null,
-   "id": "538b4d35",
+   "id": "d06ff02b",
    "metadata": {
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@@ -54,7 +54,7 @@
   {
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    "execution_count": null,
-   "id": "37038ffa",
+   "id": "205bb992",
    "metadata": {
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@@ -79,7 +79,7 @@
   {
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    "execution_count": null,
-   "id": "2b2f6f96",
+   "id": "54645c2a",
    "metadata": {
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@@ -102,7 +102,7 @@
   {
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    "execution_count": null,
-   "id": "95be5f23",
+   "id": "ef54fb58",
    "metadata": {
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@@ -121,7 +121,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "91c32437",
+   "id": "351d0784",
    "metadata": {
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@@ -140,7 +140,7 @@
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   {
    "cell_type": "markdown",
-   "id": "fa35b774",
+   "id": "5102d6e0",
    "metadata": {
     "papermill": {
      "duration": null,
@@ -158,7 +158,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8257d3d6",
+   "id": "8d73552f",
    "metadata": {
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@@ -177,7 +177,7 @@
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   {
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-   "id": "0701d3b2",
+   "id": "e62fcbb7",
    "metadata": {
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@@ -195,7 +195,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a89b6b54",
+   "id": "473e50e8",
    "metadata": {
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@@ -214,7 +214,7 @@
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   {
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+   "id": "4fd63d1c",
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@@ -232,7 +232,7 @@
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-   "id": "4e9aaa58",
+   "id": "93f3ff41",
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@@ -279,8 +279,8 @@
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+   "duration": 0.008946,
+   "end_time": "2025-11-21T17:51:43.301909",
    "environment_variables": {},
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    "input_path": "/Users/rorro6787/Desktop/SAES/SAES/html/notebooks/multiobjective_fronts2D.ipynb",
@@ -292,7 +292,7 @@
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     "references": "tests/test_data/references"
    },
-   "start_time": "2025-11-08T18:27:09.867611",
+   "start_time": "2025-11-21T17:51:43.292963",
    "version": "2.6.0"
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diff --git a/tests/htmls/fronts3D.ipynb b/tests/htmls/fronts3D.ipynb
index ede6a3a..3801e52 100644
--- a/tests/htmls/fronts3D.ipynb
+++ b/tests/htmls/fronts3D.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ad13ebbd",
+   "id": "87525f32",
    "metadata": {
     "papermill": {
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@@ -29,7 +29,7 @@
   {
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-   "id": "e2bba7a2",
+   "id": "2361787d",
    "metadata": {
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@@ -54,7 +54,7 @@
   {
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    "execution_count": null,
-   "id": "e343abd8",
+   "id": "ec784710",
    "metadata": {
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@@ -79,7 +79,7 @@
   {
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    "execution_count": null,
-   "id": "03238fbb",
+   "id": "cda5891b",
    "metadata": {
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@@ -102,7 +102,7 @@
   {
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    "execution_count": null,
-   "id": "1079af49",
+   "id": "def236ec",
    "metadata": {
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@@ -121,7 +121,7 @@
   {
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    "execution_count": null,
-   "id": "1fa1a174",
+   "id": "42eac1f9",
    "metadata": {
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@@ -140,7 +140,7 @@
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   {
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-   "id": "cb2a4c75",
+   "id": "a1e1fbdd",
    "metadata": {
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@@ -158,7 +158,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "90bef33f",
+   "id": "ba212c32",
    "metadata": {
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@@ -177,7 +177,7 @@
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   {
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-   "id": "5f7845c3",
+   "id": "68637e6b",
    "metadata": {
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@@ -195,7 +195,7 @@
   {
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-   "id": "ab349d32",
+   "id": "d993e125",
    "metadata": {
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@@ -214,7 +214,7 @@
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   {
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+   "id": "f6d44926",
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@@ -232,7 +232,7 @@
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-   "id": "48b4c8f0",
+   "id": "71546899",
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@@ -279,8 +279,8 @@
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+   "duration": 0.007209,
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    "input_path": "/Users/rorro6787/Desktop/SAES/SAES/html/notebooks/multiobjective_fronts3D.ipynb",
@@ -292,7 +292,7 @@
     "metrics": "tests/test_data/multiobjectiveMetrics.csv",
     "references": "tests/test_data/references"
    },
-   "start_time": "2025-11-08T18:27:09.889440",
+   "start_time": "2025-11-21T17:51:43.383823",
    "version": "2.6.0"
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diff --git a/tests/htmls/frontsND.ipynb b/tests/htmls/frontsND.ipynb
index 52ae382..f94702e 100644
--- a/tests/htmls/frontsND.ipynb
+++ b/tests/htmls/frontsND.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0713ffd5",
+   "id": "fa39921a",
    "metadata": {
     "papermill": {
      "duration": null,
@@ -30,7 +30,7 @@
   {
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    "execution_count": null,
-   "id": "183e2e9e",
+   "id": "4ff05456",
    "metadata": {
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@@ -55,7 +55,7 @@
   {
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    "execution_count": null,
-   "id": "bd72ca07",
+   "id": "9dcc2e24",
    "metadata": {
     "papermill": {
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@@ -80,7 +80,7 @@
   {
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-   "id": "2a0af258",
+   "id": "b14030b3",
    "metadata": {
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@@ -103,7 +103,7 @@
   {
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-   "id": "641a16d0",
+   "id": "e44b31e8",
    "metadata": {
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@@ -122,7 +122,7 @@
   {
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-   "id": "cd593de9",
+   "id": "95f5a802",
    "metadata": {
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@@ -141,7 +141,7 @@
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   {
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-   "id": "a54f8785",
+   "id": "356ad35e",
    "metadata": {
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@@ -159,7 +159,7 @@
   {
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-   "id": "13d894f3",
+   "id": "4ece2b81",
    "metadata": {
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@@ -178,7 +178,7 @@
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+   "id": "d4ea0fd1",
    "metadata": {
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@@ -196,7 +196,7 @@
   {
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-   "id": "cd3a8ba9",
+   "id": "67be1138",
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@@ -215,7 +215,7 @@
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@@ -233,7 +233,7 @@
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@@ -280,8 +280,8 @@
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    "environment_variables": {},
    "exception": null,
    "input_path": "/Users/rorro6787/Desktop/SAES/SAES/html/notebooks/multiobjective_frontsND.ipynb",
@@ -294,7 +294,7 @@
     "metrics": "tests/test_data/multiobjectiveMetrics.csv",
     "references": "tests/test_data/references"
    },
-   "start_time": "2025-11-08T18:27:09.915072",
+   "start_time": "2025-11-21T17:51:43.483013",
    "version": "2.6.0"
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diff --git a/tests/htmls/no_fronts.ipynb b/tests/htmls/no_fronts.ipynb
index b0c462c..98bb6a9 100644
--- a/tests/htmls/no_fronts.ipynb
+++ b/tests/htmls/no_fronts.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "959e7cfa",
+   "id": "a0640d5d",
    "metadata": {
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@@ -27,7 +27,7 @@
   {
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    "execution_count": null,
-   "id": "8fd51bdd",
+   "id": "8bdca087",
    "metadata": {
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@@ -52,7 +52,7 @@
   {
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    "execution_count": null,
-   "id": "ff774d79",
+   "id": "1da4e750",
    "metadata": {
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@@ -77,7 +77,7 @@
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-   "id": "54237675",
+   "id": "0b0718f7",
    "metadata": {
     "papermill": {
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@@ -97,7 +97,7 @@
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   {
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-   "id": "ed79c690",
+   "id": "5cbb7959",
    "metadata": {
     "papermill": {
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@@ -115,7 +115,7 @@
   {
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-   "id": "04c90694",
+   "id": "ccb03460",
    "metadata": {
     "papermill": {
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@@ -133,7 +133,7 @@
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   {
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+   "id": "bd77d000",
    "metadata": {
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@@ -151,7 +151,7 @@
   {
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-   "id": "75d4c58f",
+   "id": "5f8bceff",
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@@ -170,7 +170,7 @@
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+   "id": "036518f9",
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@@ -188,7 +188,7 @@
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-   "id": "69af9c60",
+   "id": "607f3feb",
    "metadata": {
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@@ -207,7 +207,7 @@
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   {
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+   "id": "d3aae9a8",
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@@ -225,7 +225,7 @@
   {
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-   "id": "b2807f83",
+   "id": "b85bc1d1",
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@@ -244,7 +244,7 @@
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+   "id": "fbb9d0a1",
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@@ -262,7 +262,7 @@
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-   "id": "f3dff728",
+   "id": "44e0e451",
    "metadata": {
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@@ -281,7 +281,7 @@
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+   "id": "6f731dd1",
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@@ -299,7 +299,7 @@
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-   "id": "6a30b377",
+   "id": "5352be8d",
    "metadata": {
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@@ -318,7 +318,7 @@
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   {
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+   "id": "c90a934f",
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@@ -336,7 +336,7 @@
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-   "id": "7dc700e3",
+   "id": "49515d18",
    "metadata": {
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@@ -355,7 +355,7 @@
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   {
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+   "id": "d85819dc",
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@@ -373,7 +373,7 @@
   {
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-   "id": "cf19114d",
+   "id": "7b90e8b1",
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@@ -411,8 +411,8 @@
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   "papermill": {
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+   "duration": 0.005167,
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    "input_path": "/Users/rorro6787/Desktop/SAES/SAES/html/notebooks/multiobjective_optimization.ipynb",
@@ -422,7 +422,7 @@
     "metric": "HV",
     "metrics": "tests/test_data/multiobjectiveMetrics.csv"
    },
-   "start_time": "2025-11-08T18:27:09.929201",
+   "start_time": "2025-11-21T17:51:43.505256",
    "version": "2.6.0"
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