Intelligent Systems II · TEE · papers
Q1(a) Traditional versus evolutionary optimization [5]
Re-examination · 25 Jun 2024
Actual historical paper ·100 marks attempted /140 offered · Q1 compulsory + any four of Q2–Q7. Source defects and solution assumptions are explicitly identified.
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.