Source Of Summary:

Lec.6.D. M. spring 2024.pdf

1. Genetic Algorithms (GA) 🧬

• Definition: A type of machine-learning technique that mimics the biological process of evolution to perform search and optimization, based on Darwin’s Principle of Natural Selection.
• Origin: Inspired by Darwin’s “Survival of the Fittest” favorable characteristics propagate over generations 🌱

2. Principle of Natural Selection 🌱

• Motto: “Select The Best, Discard The Rest” 🎯
• Process:
  • Variation arises via random mutations
  • Selection retains higher-fitness individuals
  • Reproduction propagates favorable genes

3. Biological Example: Giraffes 🦒

• Observation: Giraffes with slightly longer necks could feed on higher leaves when lower ones were gone 🏞️
• Outcome:
  • Favorable Mutation (longer neck) initially rare but propagated over generations
  • Today’s giraffes uniformly have this trait ✅🔄

4. Evolution Through Natural Selection 🔄

Initial Population of Animals
        ↓
Struggle for Existence — Survival of the Fittest
        **↓**
Surviving Individuals Reproduce & Propagate Favorable Traits
        **↓**
Millions of Years of Iteration
        **↓**
Evolved Species (**Trait Becomes Established**)

5. How GAs Simulate Evolution 🤖

5.1 Basic Workflow

1. Initialize Population of candidate solutions (called chromosomes)
2. Evaluate each chromosome’s fitness
3. Selection: choose elite chromosomes for reproduction
4. Crossover: exchange genetic material between parents
5. Mutation: randomly alter genes in offspring
6. Replace or augment population with new generation
7. Repeat until a termination criterion is met 🎯