# Q1(a) Traditional versus evolutionary optimization [5]

Subject: Intelligent Systems II

Exam: TEE

Reader: https://deep-learning-study-room.vercel.app/is2/#tee/papers/paper-2024-reexam/q1a

Canonical: https://deep-learning-study-room.vercel.app/read/is2/tee/papers/paper-2024-reexam--q1a/

Actual historical paper ·100 marks attempted /140 offered · Q1 compulsory + any four of Q2–Q7. Source defects and solution assumptions are explicitly identified.

**Question. Q1(a)** [5 marks · 2024-06-25]

Compare traditional optimization and evolutionary optimization algorithms. Illustrate the basic framework of evolutionary algorithms.

### Answer

**Optimization** searches for a feasible candidate that maximizes or minimizes an objective. Evolutionary algorithms use a population, fitness-based selection and variation inspired by evolution.

| Aspect | Traditional methods (typical examples) | Evolutionary methods |
| --- | --- | --- |
| Search state | Often one current candidate, e.g. gradient descent | Usually a population of candidates |
| Information | Gradient/Hessian in derivative methods; not required by all traditional methods | Primarily objective/fitness evaluations |
| Variation | A mathematically derived step or deterministic search rule | Crossover and mutation with stochastic choices |
| Landscape | Strong efficiency/guarantees possible under assumptions such as convexity | Useful for discrete, non-smooth, mixed or multimodal search |
| Guarantee | Depends on the algorithm and assumptions | No general finite-run global-optimum guarantee |
| Cost | May require fewer evaluations on suitable smooth problems | Often needs many evaluations and parameter tuning |

**Framework:** encode candidates → initialize feasible population → evaluate fitness → select parents → crossover → mutate → evaluate offspring → choose survivors → repeat until stopping criterion → return best observed candidate.

For example, in a delivery-route GA a chromosome is a permutation of cities; fitness can be inverse route length. Shorter valid tours are favored. A swap mutation preserves city uniqueness. Selection exploits good candidates, while variation introduces exploration. Do not claim every traditional method requires derivatives or every evolutionary method escapes all local optima.