Prismo | Branches of AI (Part 3)
Planning, Heuristics & Genetic Programming: How AI Solves Problems and Evolves
We’ve reached the final part of our “Branches of AI” series. In Part 1, we explored how AI learns from data (Machine Learning) and follows rules (Logic-Based AI). In Part 2, we saw how AI finds and remembers information through Search and Knowledge Representation.
Now, we’re wrapping it up with three fascinating branches that show how AI plans, adapts, and evolves: Planning, Heuristics, and Genetic Programming.
AI in Plain Language
Planning: Think of this as AI’s ability to make to-do lists. Planning algorithms help machines figure out what steps to take to reach a goal, like a robot deciding which boxes to move first in a warehouse or an calendar app scheduling your day to avoid conflicts.
Heuristics: A heuristic is an educated guess. When a perfect solution takes too long to calculate, AI uses shortcuts that are “good enough.” According to Britannica, heuristics are methods for solving problems quickly when exhaustive search isn’t practical. For example, if you’re trying to solve a maze, instead of checking every possible path, a heuristic might tell you to “stay close to the right wall” — not perfect, but practical.
Genetic Programming: Inspired by evolution, this branch teaches AI to “breed” better solutions over time. It starts with random possibilities, tests them, keeps what works best, and combines those to create new generations of solutions (see Stanford’s overview of Genetic Programming for more background).
Why It Matters for Professionals
These branches of AI show up everywhere once you start looking. Planning helps systems like logistics platforms, project managers, and personal assistants anticipate needs and optimize workflows. Heuristics drive quick decision-making in business, from route optimization in delivery networks to resume screening in HR software. They allow systems to act fast when time or data are limited. Meanwhile, Genetic Programming fuels creative problem-solving in fields like engineering, marketing, and design, where evolving a better product, structure, or layout can make all the difference. Together, these branches illustrate how AI manages uncertainty, trade-offs, and long-term strategy — the very qualities that make intelligent systems feel intelligent.
Real-World Examples
Planning in action:
Google Calendar suggesting meeting times that fit everyone’s schedule.
Warehouse robots deciding how to move items efficiently.
AI in games planning enemy movements based on your behavior.
Heuristics in action:
Navigation apps estimating travel time using traffic patterns.
Recruiting software scanning resumes for keywords as a shortcut to identify candidates.
Spam filters prioritizing certain signals (“subject line + sender + word frequency”).
Genetic Programming in action:
NASA using evolutionary algorithms to design efficient antennas.
Financial algorithms optimizing investment portfolios through iterative testing.
AI art or music tools generating creative outputs based on evolving models.
A Few Limitations to Keep in Mind
Even the smartest AI approaches have their flaws. Planning can struggle when unexpected variables change mid-plan, like when weather delays throw off a logistics schedule. Heuristics, while fast, can sometimes overlook better options because they rely on shortcuts that are “good enough,” not perfect. And Genetic Programming can produce strange or impractical results if the “fitness rules” (the criteria that decide what counts as a good solution) aren’t well-defined. In other words, even evolution can get a little weird.
Try It Yourself
Open an AI app like ChatGPT or Gemini.
Ask: “Plan a weekend trip that includes outdoor activities, good food, and minimal driving.” You’ll likely get a step-by-step plan, maybe with a timeline, restaurant ideas, and a route. Notice how the AI is breaking a larger goal into smaller, ordered steps. That’s Planning in action: the system is reasoning about how to reach a goal efficiently.
Then ask: “What’s a quick rule of thumb for finding good restaurants in a new city?” The answer will probably include shortcuts or general advice, like “look for places with lots of recent reviews” or “avoid menus that are too long.” That’s Heuristics: the AI isn’t calculating the perfect answer; it’s offering a good enough strategy based on patterns it’s seen.
Finally, ask: “Explain how evolution could improve restaurant recommendations over time.” Here, the AI will talk about testing and improving, maybe by “keeping” the best-performing recommendations and “combining” them to make new ones. That’s Genetic Programming: the system is describing how solutions evolve through trial, error, and adaptation.
Notice how each question calls on a different kind of reasoning.
Think About It
Where do you use these strategies in your own work or life?
Planning: Making a project timeline or trip itinerary.
Heuristics: Relying on experience or “gut feeling” to make quick decisions.
Genetic Programming: Testing versions of a design, ad, or strategy until you find what works best.
Next time you organize your day, take a shortcut, or refine an idea, you’ll know — you’re thinking like an AI.
The Takeaway
These three branches show how AI goes beyond learning and memory — it plans, guesses, and evolves. Together with the earlier branches, they complete the map of how artificial intelligence imitates (and sometimes improves on) human reasoning.
We’ve now explored all the main branches that form the foundation of artificial intelligence. As we move forward, Prismo will keep shining light on the ideas that drive today’s AI — connecting theory with understanding, one short lesson at a time.
💡 Curious about how this fits into your own career? Drop us a note — we love a good AI chat.
