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Give the name of the
- a. Local beam search with k = 1.
- b. Local beam search with one initial state and no limit on the number of states retained.
- c. Simulated annealing with T = 0 at all times (and omitting the termination test).
- d. Simulated annealing with T = ∞ at all times.
- e. Genetic algorithm with population size N = 1.
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Explanation of Solution
a.
The local beam search with “k=1” is hill-climbing search.
b.
- Local beam search with one initial state and no limit on the number of states retained resembles with Breadth-First search.
- In breadth first search, before adding the next layer it adds one complete layer nodes.
- Starting from one state, the algorithm would be essentially identical to breadth-first search except that each layer is generated all at once.
c.
Simulated annealing with “T=0” at all time:
- There is a fact that termination step would be triggered immediately. Ignoring this fact, the search would be identical to first choice hill climbing.
- This is because; every downward successor would be rejected with probability 1.
d.
Simulated annealing with “T = ∞” at all times is a random-walk search, it always accepts a new state.
e.
Generic algorithm with population size “N=1”:
- The two selected parents will be same individual, if the population size is “1”.
- The crossover yields an exact copy of individuals. Here, the mutation chance occurs.
- Thus, the algorithm executes a random walk in the space of individuals.
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