Lære From Evolution to Adaptive Immunity | Fundamentals of Bio-Inspired Algorithms

Definition

Adaptive immunity is a biological process that enables an organism to recognize, remember, and respond more effectively to threats over time. Unlike evolution—which improves species across generations—adaptive immunity strengthens within the lifetime of a single organism. It uses memory and selective response to quickly neutralize known pathogens, illustrating another form of biological intelligence.

In computational terms, this means algorithms can learn from past encounters and adapt dynamically, without requiring full evolutionary cycles. Just as the immune system identifies and remembers harmful invaders, some bio-inspired algorithms can detect and respond to patterns in real time. This concept leads to the family of Artificial Immune Systems (AIS) — models that use immune-like mechanisms for pattern recognition, anomaly detection, and optimization.

Evolution vs. Immunity

Example: Memory and Adaptation

Here's a small Python analogy: we simulate how a system “learns” to recognize known patterns and react faster the next time.


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import random
import time

# Initial set of known patterns (empty at start)
memory = set()
patterns = ["virus", "bacteria", "dust", "virus", "virus", "pollen"]

for pattern in patterns:
    print(f"\nEncountered: {pattern}")
    if pattern in memory:
        print("Recognized from memory — quick neutralization!")
    else:
        print("Unknown pattern — analyzing...")
        time.sleep(0.5)  # Simulate slower response
        memory.add(pattern)
        print("Stored in memory for future recognition.")

This simple script illustrates how adaptive immunity differs from evolution: the system doesn't evolve across generations — it remembers and improves instantly through experience.

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