Evolutionary Algorithms: A Plain‑English Guide

June 1, 2025

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5 min read

AI Generated

AI filmmaking

Interesting

Contributors

Justin Trantham

Prime Automator & Co-Founder

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The Big Idea in One Sentence

Let a population of possible solutions compete, keep the best performers, mix their “genes,” and repeat until you get an automation—or model—that works noticeably better.

That’s it. Everything else is detail. Here are the five concepts you need to recognise them in the wild:
Where EAs Already Shine

Product & process design - From aircraft wings to car chassis.
AI behaviour in games - Evolving non‑player characters that feel human.
Optimising invisible parameters - Scheduling train timetables, tuning hyper‑parameters of language models, or even shaving seconds off a robotic arm’s pick‑and‑place cycle.

These examples come straight from the academic world featured in the introductory video—and they map cleanly onto business automation challenges.

Step	What happens	Everyday analogy
1 • Initialization	Start with many random versions of the thing you’re trying to optimise (a business‑process flow, a language model, a bridge design).	Brainstorm 50 rough drafts instead of one perfect first draft.
2 • Evaluation (Fitness)	Score each version against a goal—speed, accuracy, cost savings, etc.	Taste‑test several cookie recipes and rate them.
3 • Selection	Keep the top scorers; retire the rest.	Short‑list your five favourite cookies.
4 • Crossover & Mutation	Combine pieces of good versions (crossover) and sprinkle in small random tweaks (mutation) to create fresh candidates.	Blend the best parts of two cookies, then add a surprise pinch of salt.
5 • Iteration & Termination	Repeat the cycle until you hit a target score or see no further improvement.	Keep baking until your testers can’t tell which batch is better.

How This Helps FlowDevs Clients

Faster discovery of best‑fit workflows
Instead of hand‑tuning every condition in a Power Automate flow, an EA can explore thousands of variations overnight and surface the most efficient path.
Continuous improvement
Set the algorithm to re‑run weekly on fresh data so your automation keeps up as volumes, customers, or regulations change.
Confidence before rollout
Because each candidate is scored against your real KPIs (speed, cost, error rate), you have measurable evidence that the chosen flow is better than the baseline.

When to Consider an EA with FlowDevs

Scenario	Why EA Helps
You have many variables and no clear “right” combination (think complex approvals, multi‑system integrations).	EAs excel at high‑dimensional search where rule‑of‑thumb tuning stalls out.
Historical data is noisy or incomplete.	Mutation introduces fresh ideas that a purely data‑driven optimiser might never test.
You need to balance several objectives at once (speed and cost and compliance).	EAs can optimise for multiple fitness scores simultaneously.

Getting Started: No PhD Required

Define a fitness score that matters to you – e.g., seconds saved per transaction.
Let FlowDevs set up an “evolution loop” using our automation framework—one that spins up variations, tests them safely in a sandbox, and promotes the winners.
Monitor the dashboard; when the improvement curve levels off, decide whether to lock in the champion flow or launch another generation with new constraints.

Key Takeaways

Evolutionary algorithms are optimisation engines, not just academic curiosities.
They complement FlowDevs’ mission to “improve, empower, and automate” by discovering better‑than‑human configurations in complex workflows.
You don’t need to understand the math—only the five‑step cycle and the outcome: quantifiably smarter automations.

Ready to explore whether an EA can sharpen your next Power Platform or custom‑code project? Book a discovery call with FlowDevs today and let’s evolve your workflow together.

References

Evolutionary Algorithms - Dr. Shahin Rostami

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