Search and Evolutionary Computation

Question 1

Evolutionary Computation

Answer 1

nature inspired” AI
algorithms for global optimization, a family of population-based trialand-error problem solvers with a metaheuristic or stochastic
optimisation character.

Question 2

Greedy Search/Best-First Search

Answer 2

use the heuristic function ℎ(𝑛) to
choose the next node that appears closest to the goal (best first)

Question 3

A* Search

Answer 3

uses both the actual cost from the start to the current
node 𝑔(𝑛) and the heuristic cost to the goal ℎ(𝑛)

Question 4

Hill Climbing

Answer 4

iteratively improves a single solution by moving to a
better neighboring solution until no better neighboring solution can be
found

Question 5

Simulated Annealing

Answer 5

explore the solution space by probabilistically
accepting worse solutions

Question 6

Beam Search:

Answer 6

heuristic search algorithm, similar to BFS, but limits
the number of nodes kept at each depth level to a fixed number 𝑘
(beam width)

Question 7

Key components:

Answer 7

representation. fitness function. selection. Genetic Operators. other params(pop size, mating pool size.)