Ingredients (makes ~3 servings, all in one pot):

• 30 g dry rolled oats
• 150 g dry lentils (rinsed)
• 150 g canned chickpeas (drained)
• 100 g brown rice (uncooked)
• 150 g broccoli florets (fresh or frozen)
• 100 g carrot, chopped
• 100 g sweet potato, diced
• 100–150 g tomato (fresh chopped or canned crushed)
• 100 g onion (1 medium), chopped
• 2 garlic cloves (about 6 g), minced
• 1 tsp fresh ginger (or 2 g dried)
• 1 tsp ground turmeric
• black pepper (Essential for turmeric)
• 1 tsp ground cumin, 1 tsp coriander, ½ tsp cinnamon
• 800 mL water or low-sodium vegetable broth (add more if needed)
• Mixed Italian Herbs (dry)

Stir in at the end (Optional):

• LSA (Linseed, Sunflower, Almond)
• FHC (Flaxmeal, Hemp, Chia)
• sunflower seeds, pumpkin seeds, sesame seeds
• mixed nuts
• Fresh lemon juice (blunt the glycemic response)
• Fresh herbs (parsley/coriander)
• Hemp seeds
• KimChi
• Avocado
• Beetroot
• 100 g leafy greens (spinach or kale)
• 1 Tbsp extra-virgin olive oil (15 mL)

Nutrition (approximate per total batch):

Calories: ~1,870 kcal (for whole pot)

Protein: ~75 g (40% daily value) – from lentils, chickpeas, flax medicalnewstoday.com

Carbohydrates: ~300 g (of which ~66 g fiber) medicalnewstoday.com (low-glycemic from beans/grains)

Fat: ~48 g (mostly unsaturated from olive oil/flaxseed)

Fiber: ~66 g (≫ recommended 30 g; high soluble fiber lowers LDL and blood sugar) medicalnewstoday.com

Ingredients (makes ~3 servings, all in one pot):

• 150 g dry red lentils (rinsed)
• 150 g canned chickpeas (drained)
• 100 g brown rice (uncooked)
• 150 g broccoli florets (fresh or frozen)
• 100 g leafy greens (spinach or kale)
• 100 g carrot, chopped
• 100 g sweet potato, diced
• 100–150 g tomato (fresh chopped or canned crushed)
• 100 g onion (1 medium), chopped
• 2 garlic cloves (about 6 g), minced
• 1 tsp fresh ginger (or 2 g dried)
• 1 tsp ground turmeric
• 1 tsp ground cumin, 1 tsp coriander, ½ tsp cinnamon, pinch of black pepper (optional)
• 1 Tbsp extra-virgin olive oil (15 mL)
• 1 Tbsp ground flaxseed (10–15 g)
• 800 mL water or low-sodium vegetable broth (add more if needed)
• Fresh herbs (parsley/coriander) or lemon juice to taste (optional garnish)

Nutrition (approximate per total batch):

Calories: ~1,870 kcal (for whole pot)

Protein: ~75 g (40% daily value) – from lentils, chickpeas, flax medicalnewstoday.com

Carbohydrates: ~300 g (of which ~66 g fiber) medicalnewstoday.com (low-glycemic from beans/grains)

Fat: ~48 g (mostly unsaturated from olive oil/flaxseed)

Fiber: ~66 g (≫ recommended 30 g; high soluble fiber lowers LDL and blood sugar) medicalnewstoday.com

Thought I would give ChatGPT’s Deep Research option a try today.

With the Deep Research option instead of asking a question and getting an immediate response, the question is submitted and chatgpt will work on the problem for a number of minutes checking a lot of different sources online and then eventually coming back with an answer, so the deep research prompt I gave it was :

“theoretically based off foods available in coles and woolworths in sydney australia, what is the least amount of ingredients for the least amount of money that would provide all the required micronutrients vitamins and minerals a 39 year old adult male would require to be fit and healthy? give me that list of foods”

Chatgpt responded with :

To find the cheapest and most minimal list of ingredients from Coles and Woolworths in Sydney that covers all essential micronutrients for a 39-year-old male, could you please clarify the following:

1. Do you have any dietary restrictions (e.g., vegetarian, vegan, gluten-free)?
2. Should this food list cover macronutrients (calories, protein, fat, carbs) too, or only vitamins and minerals?
3. Are you looking for daily consumption cost, or weekly/monthly affordability?
4. Should the diet be sustainable for long-term daily use or is a short-term solution okay?

With that info, I can tailor the food list more accurately to your needs.”

My response :

1. Lactose intolerant
2. should cover macronutrient requirements and micronutrient requirements
3. lets go daily
4. short-term”

Chatgpt :

Great. I’ll look into the cheapest combination of foods available at Coles and Woolworths in Sydney that can meet all the daily macro- and micronutrient requirements for a 39-year-old lactose-intolerant male. This list will prioritize minimal ingredients and low cost while ensuring full nutritional coverage for short-term use.

I’ll let you know as soon as I have a complete list ready for you.

Research completed in 7m · 37 sources · 75 searches

Chatgpt thought about this problem for 7 minutes then returned the following output and also provided a URL to view this output :

https://chatgpt.com/s/dr_6816c6fe13cc8191a197cefa405bd741

Minimal-Cost Complete Diet (39‑year-old Male, Lactose-Free)

To meet the estimated daily needs of a 39‑year-old male (≈2,300–2,500 kcal) with minimal cost and ingredients, we use inexpensive, nutrient‑dense staples available at Coles/Woolworths. The menu below provides all macronutrients (protein ≈96 g, fat ≈64 g, carbs ≈343 g, fiber ≈67 g) and most essential vitamins/minerals at or above Australian RDI levels (e.g. iron, folate, vitamin C, A, K, B‐vitamins) healthline.com medicalnewstoday.com . (Fiber intake ≈67 g greatly exceeds the 25–30 g recommendation dietitiansaustralia.org.au .) All dairy is omitted (or replaced by fortified alternatives). Total cost is roughly $4–5 AUD/day. Key nutrition sources include grains, legumes, eggs and vegetables; citrus fruit supplies vitamin C; iodized salt or seafood can provide iodine. (Calcium from this diet is only ~450 mg – below the ~1,000 mg RDI betterhealth.vic.gov.au – so a calcium-rich add‑on (e.g. fortified soy milk, tofu or supplement) is recommended for longer-term use.)

Rolled oats (Coles/Woolies brand) – 150 g dry (≈1.5 cups).

Provides ≈570 kcal, ≈20 g protein, 84 g carbs (≈16 g fiber), 14 g fat woolworths.com.au woolworths.com.au . Rich in complex carbs, fiber and B vitamins. Cost ≈$0.30.

White rice (medium-grain) – 160 g dry (~0.8 cup).

Provides ≈570 kcal, ≈11 g protein, 127 g carbs (virtually no fiber) calorieking.com . Cheap staple; when combined with lentils yields a complete amino acid profile. Cost ≈$0.25.

Lentils (dried, split or whole) – 100 g dry.

Provides ≈350 kcal, ≈26 g protein, 60 g carbs and 30 g fiber fatsecret.com . Very high in plant protein, fiber, folate (≈90% DV per cooked cup healthline.com ) and iron (≈7.5 mg, 42% DV fatsecret.com ). One of the cheapest protein sources ($0.40/100 g).

Eggs (large) – 3 eggs (≈150 g).

Provides ≈230 kcal, 18 g protein, 15 g fat. They deliver high-quality protein and key micronutrients: choline and B12 (≈3–4 μg, >100% RDI), vitamin A, D and E pmc.ncbi.nlm.nih.gov . Extremely cost-efficient for protein and vitamins pmc.ncbi.nlm.nih.gov . Cost ≈$0.75.

Peanut butter (smooth) – 2 tbsp (32 g).

Provides ≈190 kcal, 8 g protein, 16 g fat, 6 g carbs, 2.6 g fiber (per 32 g). Supplies healthy monounsaturated fats plus many micronutrients: vitamin E (≈60% DV/100g), niacin (B3, 84% DV/100g), B6, folate, magnesium (37% DV/100g) and copper, manganese healthline.com . (Choose “natural” peanut butter with just peanuts/salt.) Cost ≈$0.17.

Dark leafy greens (e.g. spinach, kale) – 200 g raw (≈1 large bag frozen).

Only ≈46 kcal, but very rich in vitamin C, vitamin A (beta-carotene), vitamin K, folate and iron. For example, spinach is “rich in iron, vitamins C and E, potassium, and magnesium” medicalnewstoday.com and provides some calcium (≈100 mg/200g, though absorption is low). Cost ≈$0.80.

Carrots or mixed vegetables – 200 g raw.

Adds vitamins (especially A from beta‑carotene), potassium and extra fiber (≈5–6 g). E.g. 200 g carrots give ≈80 kcal. Carrots are very cheap (≈$0.20). Fruit (citrus) – 2 medium oranges (≈320 g total). Provides ≈150 kcal, ≈146 mg vitamin C (about 2–3× RDI) healthline.com , plus some potassium and fiber (8 g). Oranges are inexpensive ($1.60 for two). (Any citrus or other vitamin‑C fruit can substitute; vitamin C is water-soluble so abundant fruit/veg cover it easily eatforhealth.gov.au .)

Yeast spread (Vegemite/nutritional yeast) – 10 g (≈1 tsp).

Virtually no calories but a very dense source of B‑vitamins. Provides thiamin (B1), riboflavin (B2), niacin (B3) and often added B6/B12. Helps ensure B‑vitamin and B12 intake in this diet. Cost ≈$0.18.

Iodized salt – ~2 g.

Supplies iodine (bread is iodized in AUS, but adding iodized salt ensures ~70–140 μg iodine, ~50–100% of RDI). (Alternatively, a small portion of seaweed or canned fish can cover iodine.)

Macronutrient and Cost Summary:

The above daily quantities give ≈2330 kcal (≈25% protein, 24% fat, 51% carbs by energy), ~96 g protein, ~64 g fat (mainly unsaturated), ~343 g carbs and ~67 g fiber. Total estimated cost is about $4–5 AUD per day (assuming ~$0.30 oats, $0.25 rice, $0.40 lentils, $0.75 eggs, $0.17 peanut butter, $0.80 greens, $0.20 carrots, $1.60 oranges, $0.18 Vegemite).

Micronutrients: This mix covers nearly all RDIs.

For example, lentils and greens supply ample iron and folate healthline.com , oranges and spinach provide vitamin C eatforhealth.gov.au medicalnewstoday.com , carrots and spinach supply provitamin A (vitamin A), and spinach supplies vitamin K and magnesium medicalnewstoday.com . Eggs contribute vitamin D, A and B12, peanut butter adds vitamin E, B3, B6 and magnesium healthline.com . Fiber intake (~67 g) well exceeds recommendations dietitiansaustralia.org.au , benefiting gut health. The only notable shortfall is calcium (≈450 mg); to meet the ~1,000 mg RDI betterhealth.vic.gov.au one could add a calcium-rich item (e.g. 200 mL calcium-fortified soy milk adds ≈200–300 mg, or a serving of tofu/sardines) or consider a supplement if used beyond a few weeks. ## Notes: Prepare legumes and rice in batches (soak lentils 5–10 min, simmer ~20 min). Oats can be cooked as porridge or soaked overnight. Eggs can be boiled or scrambled. Use minimal oil (≈15 g/day) for cooking (adds ~130 kcal as shown). Salt and pepper for seasoning. Vegemite can be spread thinly on toast or stirred into porridge for B‑vitamins. Carrots and oranges add variety and needed vitamins. This regimen prioritizes cost and completeness over taste/variety.

Sources:

Nutrient values and RDI guidelines are drawn from Australian nutrition references eatforhealth.gov.au dietitiansaustralia.org.au fatsecret.com healthline.com healthline.com medicalnewstoday.com healthline.com betterhealth.vic.gov.au ; food prices are based on Coles/Woolworths store-brand estimates. Citations

The 9 Healthiest Beans and Legumes You Can Eat

https://www.healthline.com/nutrition/healthiest-beans-legumes

Spinach: Nutrition and health benefits

https://www.medicalnewstoday.com/articles/270609

https://dietitiansaustralia.org.au/health-advice/fibres-your-gut-sorting-through-roughage

https://www.betterhealth.vic.gov.au/health/healthyliving/calcium

Woolworths Supermarket - Buy Groceries Online

https://www.woolworths.com.au/shop/productdetails/196293/woolworths-australian-rolled-oats?srsltid=AfmBOoqFsmVmJWV7pRg7XNf43azzsOpohuEuCwbjVXqjoAhV-nCMq_td

Woolworths Supermarket - Buy Groceries Online

https://www.woolworths.com.au/shop/productdetails/196293/woolworths-australian-rolled-oats?srsltid=AfmBOoqFsmVmJWV7pRg7XNf43azzsOpohuEuCwbjVXqjoAhV-nCMq_td

https://www.calorieking.com/au/en/foods/f/calories-in-rice-white-medium-grain-rice-raw/tBRpWbJ9TZKWTd7vxXsGWA

Calories in 100 g of Lentils and Nutrition Facts

https://www.fatsecret.com/calories-nutrition/usda/lentils?portionid=61010&portionamount=100.000

Calories in 100 g of Lentils and Nutrition Facts

https://www.fatsecret.com/calories-nutrition/usda/lentils?portionid=61010&portionamount=100.000 Eggs Are Cost-Efficient in Delivering Several Shortfall Nutrients in the American Diet: A Cost-Analysis in Children and Adults - PMC

https://pmc.ncbi.nlm.nih.gov/articles/PMC7469040/

Peanut Butter Nutrition: Is It Good for You?

https://www.healthline.com/nutrition/is-peanut-butter-bad-for-you

20 Foods That Are High in Vitamin C

https://www.healthline.com/nutrition/vitamin-c-foods

https://www.eatforhealth.gov.au/nutrient-reference-values/nutrients/vitamin-c All Sources healthline medicalnewstoday dietitia…ralia.org betterhealth.vic.gov woolworths.com calorieking fatsecret pmc.ncbi.nlm.nih eatforhealth.gov

I recently worked on creating such a UI for a Python-based genetic nutrition optimizer project. The goal was a web application where users could set optimization parameters, run the Python script via a Flask backend, and observe the optimization progress live through WebSockets (SocketIO).

Here’s what the resulting UI looks like :

You can test out the application here :

https://optimize-nutrition.onrender.com/

My initial approach involved using Claude 3.7 to help generate the necessary JavaScript code for the frontend. The core requirements included displaying user controls, a status/progress indicator, a dynamically updating chart showing the optimization score over generations, a table listing the foods in the current best solution, and another detailed table showing the corresponding micronutrient levels – all updating with each generation reported by the backend. However, integrating the asynchronous updates from SocketIO to reliably refresh all these components simultaneously proved difficult. I encountered issues synchronizing the updates across the chart and multiple tables based on the data streamed from the backend, preventing me from achieving a fully functional and stable UI despite various attempts.

As a next step in the development process, I decided to use Google’s new Gemini 2.5 Pro Experimental model with the same set of requirements and my existing Python backend code (Flask, SocketIO, and the optimization logic). I provided the model with the context of the project, the Python code structure, and the specific frontend requirements, including the need for simultaneous updates to the chart and both tables based on the generation_update event emitted by SocketIO. This approach yielded functional JavaScript code that successfully integrated with the backend.

The key code improvements primarily involved structuring the frontend JavaScript to handle the SocketIO events effectively. The generated code established clear listeners for different backend events (status_update, initial_foods, generation_update, optimization_complete, etc.). Crucially, the handler for the generation_update event was designed to parse all relevant data sent from the backend in that single event (current generation number, score, food amounts, and detailed nutrient profile) and then proceed to update all corresponding UI elements: the progress bar, the score chart, the food amounts table, and the nutrient profile table. For the dynamic tables, the logic included clearing the existing table body (tbody.innerHTML = '') before iterating through the new data for the current generation and appending the updated rows (<tr>) and cells (<td>). Gemini 2.5 Pro Experimental also provided the necessary HTML table structures and basic CSS for layout and features like scrollable tables with sticky headers.

For users interacting with the resulting web UI, here’s a breakdown of what you’ll see:

• Controls: Input fields allow setting parameters like Diet Type, Number of Foods, Generations, and Population Size before starting the optimization.
• Status & Progress: A status bar displays messages from the backend (e.g., “Running Generation X/Y”), and a progress bar fills based on the generation count.
• Score History Chart: This line graph tracks the optimization’s performance. The horizontal axis (X-axis) represents the generation number, while the vertical axis (Y-axis) shows the ‘Score’ of the best solution found in that generation. A lower score indicates a better nutritional match to the targets, so ideally, this line should trend downwards as the optimization progresses.
• Real-time Food Amounts Table: This table lists the specific foods and their corresponding amounts (in grams) that make up the best meal plan found in the most recent generation reported.
• Nutrient Profile Table: This detailed table complements the food list. It shows the calculated nutritional breakdown for the current best meal plan listed above. For each nutrient, it displays the total calculated ‘Amount’ (with units), the percentage of the target for that specific meal (‘% Meal Target’), the percentage of the total recommended daily intake (‘% Daily RDI’), and a ‘Status’ indicator (typically LOW, OK, or HIGH based on the meal target percentage). Both the food amounts and nutrient profile tables update simultaneously with each generation update.

This resulting interface now provides the intended real-time feedback, allowing observation of how the genetic algorithm refines the meal plan and its nutritional profile over successive generations.

I’ve been doing some more work on my nutrition optimisation script, the script is now running on a schedule via Github Actions, it now supports a number of diet types All, Vegan, Whole Food Plant Based, Nutrient Dense. Every hour it generates new recipes, it will look at the diet json files and read the exclusion and inclusion lists defined in the JSON file and then randomly select a random number of foods from the Australian Nutrition database csv along with their nutrition data and then the script will do a random number of generations of a genetic algorithm attempting to determine the number of grams per food item required to match as closely as possible to the recommended daily intake of essential vitamins and minerals for a 39 year old male (myself), it then saves the generated recipes in the github repo as JSON and HTML files and generates and index of these recipes and sorts them by score, the lower the score the better and the more the closer it is to matching the micronutrient RDI targets. There is a clean up at the end of the script that deletes recipes with a score that’s higher then the specified value and deletes any recipes over a total of 250 files, so over time the script will continue to generate better recipes and push out the ones with higher scores.

Challenges so far have been the Vegan and WFPB json files, trying to exclude all of the meat items, every time I think I’ve excluded them all I see a vegan recipe generated with a Camel or Emu or some type of fish in it and I need to exclude that item from the list and even more so for Whole Food Plant Based have to exclude the meat and all of the non whole food items like cakes and biscuits and packaged goods etc, so it’s still a work in progress.

I fed the optimising nutrition script into Grok and asked it to review the code it has provided some good suggestions that I will take on board :

review the logic of this script, I am a 39 year old male moderately active and want to generate recipes that meet 100% of my daily recommended intake of the essential vitamins and minerals, determine any issues in this script and any flaws

Grok AI Code Review

I’ll review your script for generating recipes that meet 100% of your daily recommended intake (RDI) of essential nutrients. As a 39-year-old moderately active male, your RDI values in the JSON look generally appropriate (based on standard guidelines like those from the NIH or USDA), though I’ll point out potential issues and suggest improvements. Here’s my analysis of the script’s logic and potential flaws:

Strengths of the Script

1. Genetic Algorithm Approach: Using a genetic algorithm (GA) to optimize nutrient intake is a solid choice for this complex, multi-variable problem. It effectively balances exploration (randomness) and exploitation (optimization toward RDI targets).
2. Flexible Nutrient Targets: The script scales RDI targets based on the number of meals, which is practical for daily planning.
3. Detailed Reporting: The JSON and HTML reports are comprehensive, providing actionable insights into food quantities, nutrient profiles, and optimization performance.
4. Penalty System: Differentiating penalties for under-RDI, over-RDI, and upper limits (UL), with special handling for water-soluble vs. fat-soluble vitamins, is a thoughtful approach to nutrient safety and balance.
5. Diet Type Support: Filtering foods based on diet types (e.g., vegan, whole food plant-based) adds versatility.

Issues and Flaws

Here are the key issues I identified in the script, along with their potential impact:

1. Missing Upper Limit (UL) Enforcement

• Problem: While the script penalizes exceeding ULs (e.g., in _calculate_nutrition_score), it doesn’t strictly enforce them. The penalty for exceeding ULs (over_ul) is randomized between 1.8 and 2.5, which may not be strong enough to prevent unsafe nutrient levels (e.g., Vitamin A at 3000 µg or Zinc at 40 mg).
• Impact: For a 39-year-old male, exceeding ULs for certain nutrients (e.g., Vitamin A, Iron) could lead to toxicity over time.
• Fix: Add a hard constraint in evaluate_solution to disqualify solutions exceeding ULs for critical nutrients, or increase the over_ul penalty significantly (e.g., 10x instead of 2.5x).

2. Energy (kJ) Handling is Incomplete

• Problem: The script treats “Energy with dietary fibre, equated (kJ)” as a nutrient with an RDI (10,878 kJ) and UL (13,389 kJ), but the logic in _calculate_nutrition_score re-reads rdi.json instead of using the passed targets dictionary. This is inconsistent and assumes the JSON file is always available.
• Impact: For a moderately active 39-year-old male, energy needs are ~10,000-11,000 kJ (2400-2600 kcal), which aligns with your RDI. However, the UL seems arbitrary and may not reflect realistic limits. Plus, the re-reading of JSON could fail if the file is missing.
• Fix: Use the targets dictionary consistently for all nutrients, including energy. Define a more realistic UL (e.g., 15,000 kJ) based on activity level and safety margins.

3. Food Quantity Range is Limited

• Problem: In create_solution, food amounts are initialized between 25-100g, and mutations adjust by ±20g. This range is too narrow to meet 100% RDI for a full day in one meal (your number_of_meals=1 setting).
• Impact: With only 25-100g per food, it’s unlikely to hit high RDIs (e.g., 3400 mg Potassium, 38g Fiber) without including more foods or larger portions.
• Fix: Increase the range (e.g., 25-500g) and adjust mutation steps proportionally. Alternatively, use a food database with serving size metadata to scale amounts realistically.

4. Nutrient Mapping Mismatch

• Problem: The nutrient_mapping parameter in optimize_nutrition is a dictionary from rdi.json, but the script uses its keys directly as column names in food_df. However, rdi.json keys (e.g., “Protein (g)”) don’t match typical food database column names (e.g., “PROTEIN”).
• Impact: This causes warnings ("Column not found") and sets missing nutrients to 0, skewing optimization results.
• Fix: Create a proper mapping dictionary (e.g., {'Protein (g)': 'PROTEIN', ...}) and pass it to optimize_nutrition. Ensure food_df columns align with this mapping.

5. Single-Meal Assumption

• Problem: You set number_of_meals=1, aiming for 100% RDI in one meal, but the script’s logic (e.g., scaling RDIs by number_of_meals) assumes multiple meals. This inconsistency could confuse results.
• Impact: A single meal hitting 100% RDI (e.g., 10,878 kJ, 91g protein) is impractical for most people due to volume and digestion limits.
• Fix: Either adjust number_of_meals to 3 (typical daily meals) and aim for ~33% RDI per meal, or explicitly design for a single meal with realistic portion constraints (e.g., max 1000g total food).

6. Random Food Selection

• Problem: The script randomly selects 10-30 foods per run (n_foods = random.randint(10, 30)), which may exclude nutrient-dense options critical for meeting RDIs (e.g., spinach for folate, salmon for omega-3s).
• Impact: This randomness reduces consistency and may fail to meet 100% RDI for some nutrients by chance.
• Fix: Pre-filter foods based on nutrient density (e.g., using nutrient_score) before random selection, or increase the pool size (e.g., 50 foods).

7. Lack of Practicality Constraints

• Problem: The optimization doesn’t consider meal practicality (e.g., taste compatibility, preparation feasibility, or total volume).
• Impact: You might get a “recipe” with 100g of broccoli, 75g of liver, and 50g of almonds—nutrient-rich but unpalatable or hard to prepare together.
• Fix: Add constraints like maximum total weight (e.g., 800g), food category diversity (e.g., at least one protein, carb, fat source), or a taste compatibility score.

8. Missing Dependencies and Error Handling

• Problem: The script assumes the presence of Release 2 - Nutrient file.xlsx, rdi.json, and diet config files (e.g., vegan.json). Missing files or mismatched formats will crash it.
• Impact: Reduces robustness for real-world use.
• Fix: Add try-except blocks around file operations and provide default fallbacks or clear error messages.

9. RDI Values for Your Profile

• Problem: Some RDI values seem off for a 39-year-old moderately active male:
  • Protein (91g): High end; typically 56-70g unless very active or muscular.
  • Vitamin D (15 µg): Matches standard, but many adults need more (20-50 µg) depending on sunlight exposure.
  • Omega-3 (1600 mg): High; typical RDI is 250-500 mg EPA/DHA, though 1600 mg aligns with some heart health guidelines.
• Impact: Over- or under-targeting nutrients could skew results.
• Fix: Verify RDIs against authoritative sources (e.g., NIH, IOM) and adjust based on your activity level (e.g., ~0.8g protein/kg body weight).

Suggestions for Improvement

1. Add a Feasibility Check: After optimization, validate that the total food weight is reasonable (e.g., 500-1000g for one meal) and nutrients are within safe ranges (RDI ≤ intake ≤ UL).
2. Incorporate Activity Level: Adjust energy and protein RDIs dynamically based on your moderate activity (e.g., use Harris-Benedict equation for BMR + activity factor).
3. Improve Nutrient Scoring: Weight nutrients by their physiological importance (e.g., prioritize deficiencies like Vitamin D or Iron over excesses of water-soluble vitamins).
4. Test with Real Data: Run the script with your food database and check if it consistently hits 100% RDI for all nutrients. Tweak population_size and generations if needed.
5. Add a Recipe Generator: Convert the food list into a practical recipe with preparation instructions (e.g., “Grilled Salmon with Broccoli and Quinoa”).

Example Fix for Key Issues

Here’s a snippet addressing some critical flaws:

# Adjust RDI for activity level (example: 80kg male, moderate activity)
basal_energy = 10 * 80 + 6.25 * 175 - 5 * 39 + 5  # Harris-Benedict BMR (~1700 kcal)
energy_rdi = basal_energy * 1.55  # Moderate activity factor (~2600 kcal = 10878 kJ)
rdi_values["Energy with dietary fibre, equated (kJ)"] = {"rdi": energy_rdi, "ul": energy_rdi * 1.3}

# Enforce UL in evaluate_solution
def evaluate_solution(solution):
    current_nutrients = {nutrient: 0 for nutrient in meal_rdi_targets}
    for food, amount in solution.items():
        for nutrient in meal_rdi_targets:
            if nutrient in foods_df.loc[food]:
                nutrient_per_gram = foods_df.loc[food][nutrient] / foods_df.loc[food]['density']
                current_nutrients[nutrient] += nutrient_per_gram * amount
    # Check ULs
    with open('rdi.json', 'r') as f:
        nutrient_data = json.load(f)
    for nutrient, value in current_nutrients.items():
        ul = nutrient_data.get(nutrient, {}).get('ul', float('inf'))
        if value > ul:
            return float('inf')  # Disqualify solution
    return _calculate_nutrition_score(current_nutrients, meal_rdi_targets, penalties)

# Wider food range
def create_solution():
    return {food: random.uniform(25, 500) for food in available_foods}

Conclusion

Your script is a strong foundation for generating nutrient-optimized recipes, but it needs refinement to ensure safety (UL enforcement), practicality (portion sizes, meal feasibility), and accuracy (nutrient mapping, RDI alignment). With the fixes above, it should better meet your goal of 100% RDI for a 39-year-old moderately active male. Let me know if you’d like help testing it with your food database or refining specific parts!

I’ve been doing some more work on my nutrition optimisation script, the script is now running on a schedule via Github Actions, it now supports a number of diet types All, Vegan, Whole Food Plant Based, Nutrient Dense. Every hour it generates new recipes, it will look at the diet json files and read the exclusion and inclusion lists defined in the JSON file and then randomly select a random number of foods from the Australian Nutrition database csv along with their nutrition data and then the script will do a random number of generations of a genetic algorithm attempting to determine the number of grams per food item required to match as closely as possible to the recommended daily intake of essential vitamins and minerals for a 39 year old male (myself), it then saves the generated recipes in the github repo as JSON and HTML files and generates and index of these recipes and sorts them by score, the lower the score the better and the more the closer it is to matching the micronutrient RDI targets. There is a clean up at the end of the script that deletes recipes with a score that’s higher then the specified value and deletes any recipes over a total of 250 files, so over time the script will continue to generate better recipes and push out the ones with higher scores.

Challenges so far have been the Vegan and WFPB json files, trying to exclude all of the meat items, every time I think I’ve excluded them all I see a vegan recipe generated with a Camel or Emu or some type of fish in it and I need to exclude that item from the list and even more so for Whole Food Plant Based have to exclude the meat and all of the non whole food items like cakes and biscuits and packaged goods etc, so it’s still a work in progress.

I fed the optimising nutrition script into Claude and asked it to review the code it has provided some good suggestions that I will take on board :

review the logic of this script, I am a 39 year old male moderately active and want to generate recipes that meet 100% of my daily recommended intake of the essential vitamins and minerals, determine any issues in this script and any flaws

Anthropic Claude response

I’ll review the script and identify any potential issues or flaws specifically for your goal of generating recipes that meet 100% of your daily recommended intake of essential vitamins and minerals as a 39-year-old moderately active male.

Overall Analysis

The script uses a genetic algorithm approach to optimize food combinations to meet nutritional requirements, which is a solid approach for this complex problem. Let’s examine specific aspects:

Strengths

• The genetic algorithm approach is appropriate for multi-objective optimization like this
• The penalties system allows for nuanced handling of different nutrients
• The code handles water-soluble vs. fat-soluble vitamins differently (appropriate since excess water-soluble vitamins are generally less concerning)
• Detailed reporting and visualization of results

Issues and Potential Improvements

1. RDI Target Splitting Assumption
   • The code divides RDI targets by the number of meals, assuming equal distribution
   • This may not be ideal for all nutrients, as some (like protein) may benefit from distribution throughout the day while others can be consumed in a single meal
2. Missing UL Values
   • Several nutrients in your JSON have RDI but no UL (Upper Limit) specified
   • The _calculate_nutrition_score function expects UL values for some calculations
   • Without UL values, the function may not properly penalize excessive nutrient intake
3. Nutrient Density Calculation
   • The nutrient density scoring doesn’t account for negative nutrients (like sodium, saturated fats) that should be limited
4. Fixed Food Amount Ranges
   • Food amounts are randomly initialized between 25-100g and mutated in ±20g increments
   • This might not be realistic for all foods (e.g., spices vs. staples)
5. Food Selection Mechanism
   • For diet-specific runs, the code randomly selects 10-30 foods from the filtered dataset
   • This might lead to suboptimal results if key nutritionally dense foods are randomly excluded
6. Missing Bioavailability Considerations
   • The script doesn’t account for nutrient interactions that affect bioavailability
   • For example, iron absorption is affected by vitamin C, zinc absorption by phytates, etc.
7. Generations and Population Size
   • Your implementation randomly selects between 10-300 generations
   • The range is very wide - 10 generations likely won’t be enough for convergence
   • Consider setting a more consistent minimum (e.g., 50+)
8. Optimization Target
   • The fitness function primarily focuses on matching RDI values but doesn’t explicitly optimize for:
     • Food variety/diversity
     • Food preferences/taste
     • Cost of ingredients
     • Seasonal availability
     • Meal compatibility (foods that work well together)
9. Practical Meal Planning
   • The script optimizes for nutrients but doesn’t group foods into practical meals
   • No consideration for cooking methods or preparation feasibility
10. Potential Implementation Issues
    • The code references start_time before defining it in the main function
    • The _is_nutrition_sufficient function is defined but never used

Specific Recommendations

1. Add Missing UL Values
   • Add upper limits for all nutrients where applicable
   • For nutrients that truly don’t have established ULs, consider setting generous but reasonable values
2. Adjust Food Amount Constraints
   • Consider categorizing foods by typical serving sizes (e.g., spices vs. vegetables)
   • Adjust the mutation ranges based on food categories
3. Enhance the Food Selection Mechanism
   • Instead of purely random selection, ensure representation from key food groups
   • Consider implementing a “seed” system where nutritionally exceptional foods are always included
4. Incorporate Bioavailability Factors
   • Add modifiers for known nutrient interactions
   • Consider time-of-day effects for certain nutrients (e.g., timing of protein intake)
5. More Strategic Generation Parameters
   • Set a higher minimum number of generations (at least 50)
   • Consider implementing early stopping if scores plateau
6. Add Meal Practicality Factors
   • Implement clustering to group compatible foods
   • Add penalties for unusual food combinations
7. Fix Implementation Issues
   • Move the start_time = time.time() into the appropriate function scope
   • Either use or remove the _is_nutrition_sufficient function

Conclusion

The script has a solid foundation for nutrient optimization but could benefit from refinements to produce more practical, varied, and enjoyable meals while still meeting nutritional requirements. The main philosophical question is whether you want to optimize purely for nutrition or balance nutrition with practical meal planning considerations.

Would you like me to elaborate on any specific aspect of this review or provide code examples for implementing any of these recommendations?

Grok AI Code Review

I’ll review your script for generating recipes that meet 100% of your daily recommended intake (RDI) of essential nutrients. As a 39-year-old moderately active male, your RDI values in the JSON look generally appropriate (based on standard guidelines like those from the NIH or USDA), though I’ll point out potential issues and suggest improvements. Here’s my analysis of the script’s logic and potential flaws:

Strengths of the Script

1. Genetic Algorithm Approach: Using a genetic algorithm (GA) to optimize nutrient intake is a solid choice for this complex, multi-variable problem. It effectively balances exploration (randomness) and exploitation (optimization toward RDI targets).
2. Flexible Nutrient Targets: The script scales RDI targets based on the number of meals, which is practical for daily planning.
3. Detailed Reporting: The JSON and HTML reports are comprehensive, providing actionable insights into food quantities, nutrient profiles, and optimization performance.
4. Penalty System: Differentiating penalties for under-RDI, over-RDI, and upper limits (UL), with special handling for water-soluble vs. fat-soluble vitamins, is a thoughtful approach to nutrient safety and balance.
5. Diet Type Support: Filtering foods based on diet types (e.g., vegan, whole food plant-based) adds versatility.

Issues and Flaws

Here are the key issues I identified in the script, along with their potential impact:

1. Missing Upper Limit (UL) Enforcement

• Problem: While the script penalizes exceeding ULs (e.g., in _calculate_nutrition_score), it doesn’t strictly enforce them. The penalty for exceeding ULs (over_ul) is randomized between 1.8 and 2.5, which may not be strong enough to prevent unsafe nutrient levels (e.g., Vitamin A at 3000 µg or Zinc at 40 mg).
• Impact: For a 39-year-old male, exceeding ULs for certain nutrients (e.g., Vitamin A, Iron) could lead to toxicity over time.
• Fix: Add a hard constraint in evaluate_solution to disqualify solutions exceeding ULs for critical nutrients, or increase the over_ul penalty significantly (e.g., 10x instead of 2.5x).

2. Energy (kJ) Handling is Incomplete

• Problem: The script treats “Energy with dietary fibre, equated (kJ)” as a nutrient with an RDI (10,878 kJ) and UL (13,389 kJ), but the logic in _calculate_nutrition_score re-reads rdi.json instead of using the passed targets dictionary. This is inconsistent and assumes the JSON file is always available.
• Impact: For a moderately active 39-year-old male, energy needs are ~10,000-11,000 kJ (2400-2600 kcal), which aligns with your RDI. However, the UL seems arbitrary and may not reflect realistic limits. Plus, the re-reading of JSON could fail if the file is missing.
• Fix: Use the targets dictionary consistently for all nutrients, including energy. Define a more realistic UL (e.g., 15,000 kJ) based on activity level and safety margins.

3. Food Quantity Range is Limited

• Problem: In create_solution, food amounts are initialized between 25-100g, and mutations adjust by ±20g. This range is too narrow to meet 100% RDI for a full day in one meal (your number_of_meals=1 setting).
• Impact: With only 25-100g per food, it’s unlikely to hit high RDIs (e.g., 3400 mg Potassium, 38g Fiber) without including more foods or larger portions.
• Fix: Increase the range (e.g., 25-500g) and adjust mutation steps proportionally. Alternatively, use a food database with serving size metadata to scale amounts realistically.

4. Nutrient Mapping Mismatch

• Problem: The nutrient_mapping parameter in optimize_nutrition is a dictionary from rdi.json, but the script uses its keys directly as column names in food_df. However, rdi.json keys (e.g., “Protein (g)”) don’t match typical food database column names (e.g., “PROTEIN”).
• Impact: This causes warnings ("Column not found") and sets missing nutrients to 0, skewing optimization results.
• Fix: Create a proper mapping dictionary (e.g., {'Protein (g)': 'PROTEIN', ...}) and pass it to optimize_nutrition. Ensure food_df columns align with this mapping.

5. Single-Meal Assumption

• Problem: You set number_of_meals=1, aiming for 100% RDI in one meal, but the script’s logic (e.g., scaling RDIs by number_of_meals) assumes multiple meals. This inconsistency could confuse results.
• Impact: A single meal hitting 100% RDI (e.g., 10,878 kJ, 91g protein) is impractical for most people due to volume and digestion limits.
• Fix: Either adjust number_of_meals to 3 (typical daily meals) and aim for ~33% RDI per meal, or explicitly design for a single meal with realistic portion constraints (e.g., max 1000g total food).

6. Random Food Selection

• Problem: The script randomly selects 10-30 foods per run (n_foods = random.randint(10, 30)), which may exclude nutrient-dense options critical for meeting RDIs (e.g., spinach for folate, salmon for omega-3s).
• Impact: This randomness reduces consistency and may fail to meet 100% RDI for some nutrients by chance.
• Fix: Pre-filter foods based on nutrient density (e.g., using nutrient_score) before random selection, or increase the pool size (e.g., 50 foods).

7. Lack of Practicality Constraints

• Problem: The optimization doesn’t consider meal practicality (e.g., taste compatibility, preparation feasibility, or total volume).
• Impact: You might get a “recipe” with 100g of broccoli, 75g of liver, and 50g of almonds—nutrient-rich but unpalatable or hard to prepare together.
• Fix: Add constraints like maximum total weight (e.g., 800g), food category diversity (e.g., at least one protein, carb, fat source), or a taste compatibility score.

8. Missing Dependencies and Error Handling

• Problem: The script assumes the presence of Release 2 - Nutrient file.xlsx, rdi.json, and diet config files (e.g., vegan.json). Missing files or mismatched formats will crash it.
• Impact: Reduces robustness for real-world use.
• Fix: Add try-except blocks around file operations and provide default fallbacks or clear error messages.

9. RDI Values for Your Profile

• Problem: Some RDI values seem off for a 39-year-old moderately active male:
  • Protein (91g): High end; typically 56-70g unless very active or muscular.
  • Vitamin D (15 µg): Matches standard, but many adults need more (20-50 µg) depending on sunlight exposure.
  • Omega-3 (1600 mg): High; typical RDI is 250-500 mg EPA/DHA, though 1600 mg aligns with some heart health guidelines.
• Impact: Over- or under-targeting nutrients could skew results.
• Fix: Verify RDIs against authoritative sources (e.g., NIH, IOM) and adjust based on your activity level (e.g., ~0.8g protein/kg body weight).

Suggestions for Improvement

1. Add a Feasibility Check: After optimization, validate that the total food weight is reasonable (e.g., 500-1000g for one meal) and nutrients are within safe ranges (RDI ≤ intake ≤ UL).
2. Incorporate Activity Level: Adjust energy and protein RDIs dynamically based on your moderate activity (e.g., use Harris-Benedict equation for BMR + activity factor).
3. Improve Nutrient Scoring: Weight nutrients by their physiological importance (e.g., prioritize deficiencies like Vitamin D or Iron over excesses of water-soluble vitamins).
4. Test with Real Data: Run the script with your food database and check if it consistently hits 100% RDI for all nutrients. Tweak population_size and generations if needed.
5. Add a Recipe Generator: Convert the food list into a practical recipe with preparation instructions (e.g., “Grilled Salmon with Broccoli and Quinoa”).

Example Fix for Key Issues

Here’s a snippet addressing some critical flaws:

# Adjust RDI for activity level (example: 80kg male, moderate activity)
basal_energy = 10 * 80 + 6.25 * 175 - 5 * 39 + 5  # Harris-Benedict BMR (~1700 kcal)
energy_rdi = basal_energy * 1.55  # Moderate activity factor (~2600 kcal = 10878 kJ)
rdi_values["Energy with dietary fibre, equated (kJ)"] = {"rdi": energy_rdi, "ul": energy_rdi * 1.3}

# Enforce UL in evaluate_solution
def evaluate_solution(solution):
    current_nutrients = {nutrient: 0 for nutrient in meal_rdi_targets}
    for food, amount in solution.items():
        for nutrient in meal_rdi_targets:
            if nutrient in foods_df.loc[food]:
                nutrient_per_gram = foods_df.loc[food][nutrient] / foods_df.loc[food]['density']
                current_nutrients[nutrient] += nutrient_per_gram * amount
    # Check ULs
    with open('rdi.json', 'r') as f:
        nutrient_data = json.load(f)
    for nutrient, value in current_nutrients.items():
        ul = nutrient_data.get(nutrient, {}).get('ul', float('inf'))
        if value > ul:
            return float('inf')  # Disqualify solution
    return _calculate_nutrition_score(current_nutrients, meal_rdi_targets, penalties)

# Wider food range
def create_solution():
    return {food: random.uniform(25, 500) for food in available_foods}

Conclusion

Your script is a strong foundation for generating nutrient-optimized recipes, but it needs refinement to ensure safety (UL enforcement), practicality (portion sizes, meal feasibility), and accuracy (nutrient mapping, RDI alignment). With the fixes above, it should better meet your goal of 100% RDI for a 39-year-old moderately active male. Let me know if you’d like help testing it with your food database or refining specific parts!

I have been working on creating an Optimising Nutrition with Genetic Algorithms python script that will randomly select a number of foods from the Australian Food Composition Database will then generate random amounts for each food in grams for a number of specified generations with each generation trying to get closer and closer to matching 100% of the Daily Recommended Intake (RDI) for each essential vitamin and mineral, so the end result will be a list of ingredients and the amount in grams you should eat to get as close as possible to the RDI.

Kind of like a whole food version of Soylent

This would be helpful for some one wanting to do a daily eating plan and wants to hit their micronutrient RDI’s.

You can view the index of recipes generated by genetic algorithm here : https://alexlaverty.github.io/optimize-nutrition/

They are sorted by score, a lower score is better, a lower score means it’s closer to matching the RDI’s and the higher the score the further away from matching the RDI’s.

I have also specified different diet types to generate for All, meaning all foods in the database, Vegan to exclude any animal products, and WFPB meaning Whole Food Plant Based which excludes food if they do not match this criteria.

The script and repo is still a work in progress, so I am open to any feedback or suggestions.

This script is designed to optimize meal planning using a genetic algorithm, which is a type of artificial intelligence inspired by natural selection.

The main goal is to select and adjust quantities of various foods to ensure that the daily nutritional requirements such as essential vitamins and minerals are met.

By using a genetic algorithm, the script evolves a set of food combinations over multiple generations, gradually improving the meal plan to better meet these nutritional targets.

Key Features and Functionality:

Food Selection and Customization:

The script begins by reading a dataset of foods, each with nutritional information. It allows for dietary customization, such as selecting only vegan or whole-food plant-based foods, by applying exclusion rules. This ensures the optimization is tailored to different diet types.

Genetic Algorithm:

The core of the optimization is the genetic algorithm. The algorithm works by simulating the process of natural selection:

It starts with a randomly selected population of meal plans (combinations of foods and their quantities). Over several generations, the algorithm “evolves” better solutions by selecting the best meal plans (those that are closer to the nutritional targets), and creating new meal plans through processes like crossover (mixing different plans) and mutation (random changes). This evolutionary process continues until an optimal or satisfactory meal plan is found.

Nutrient Calculation:

Each food item in the selected meal plans is analyzed for its contribution to meeting the daily recommended intake (RDI) of various nutrients.

The script calculates how much of each nutrient each food provides, ensuring that the final combination meets or comes close to the daily nutritional goals for essential vitamins and minerals.

Output and Reports:

After running the optimization process, the script generates detailed reports in various formats. These include:

JSON files containing the optimized meal plans, which can be saved and reused. Markdown and HTML files summarizing the results in an easily readable format.

These reports show key details like the optimization score (how well the meal plan meets the nutritional goals), the number of foods used, and how the nutrients in the meal plan are distributed across different categories (e.g., nutrients at good levels, below target, or above target).

Scalability and Flexibility:

The script is designed to be flexible, allowing it to handle various numbers of foods (from 5 to 20) and a customizable number of generations (up to 300). This means the user can adjust how complex and refined the meal plan optimization process is.

How Genetic Algorithms Help in Meal Planning:

Genetic algorithms are particularly well-suited for problems like meal optimization, where the number of possible food combinations is large and complex. Instead of manually trying every possible combination, which would be time-consuming and inefficient, the genetic algorithm simulates the natural process of evolution to “search” for the best combination efficiently.

By creating a population of meal plans and evolving them through generations, the algorithm can gradually find the combination of foods that best meets the nutritional targets, all while considering factors like variety and balance in the meal.

In summary, this script automates the process of meal planning by intelligently selecting and adjusting food items to meet nutritional needs, making use of genetic algorithms to improve the meal plans over time. It provides a flexible, automated solution for anyone looking to optimize their diet for specific health goals or dietary preferences.

Analysis of the Results

After letting the script run every hour for a few days I fed the table into ChatGPT and asked it to analyse the results.

Here is ChatGPT’s analysis of the trends and optimal values based on the provided data:

Key Observations

1. Best Score Range:
   • The lowest (best) scores are in the range 5.07 to 6.17.
   • The worst scores are above 20, with the worst being 31.85.
2. Diet Type Trends:
   • Best scores (≤6.17): Mostly “all” and “vegan” diets.
   • Worst scores (>20): Mostly “wfpb” (Whole Food Plant-Based).
   • Implication: The best-performing recipes tend to include all food groups or vegan options, while purely whole-food plant-based diets seem harder to optimize.
3. Number of Ingredients (Foods):
   • Best scores (≤6.17): Use between 10 to 20 ingredients.
   • Worst scores (>20): Often have very few ingredients (e.g., 5–8), suggesting that having more variety contributes to better nutritional balance.
4. Nutrient Balance (OK/Low/High):
   • Best scores (≤6.17): Tend to have around 17-20 nutrients at OK levels, 9-12 nutrients below target, and 0-2 above target.
   • Worst scores (>20): Have significantly more nutrients below target (e.g., 20-28 low), meaning they fail to meet nutritional needs.
5. Generations & Computation Time:
   • The best runs typically required 150-300 generations and computation times ranging 450s to 2000s.
   • The worst runs often completed in very few generations (3-50), meaning they likely didn’t converge to a well-optimized solution.

Optimal Recipe Parameters for Genetic Algorithm

If you’re generating recipes, these values would likely yield the best results:

Using this analysis I’m going to continue to tweak the script and add in more features to get recipes that more closely match the recommended daily requirements for the essential vitamins and minerals and also adding more requirements to match, calories, other things that are not essential but still desirable.

I’ve also been working on a UI which updates in real time and shows you a chart of the score as it decreases with each generation :