The answer is False.
A genetic algorithm (GA) is a search heuristic that is inspired by the process of natural selection that drives biological evolution. Genetic algorithms are typically used to search for optimal solutions to problems that are difficult to solve using traditional methods.
Stochastic beam search is a heuristic search algorithm that is used to find the best solution to a problem in a given amount of time. Stochastic beam search works by maintaining a set of promising solutions, called the beam, and expanding each solution in the beam. The solutions that are expanded are chosen randomly, but the algorithm is biased towards expanding solutions that are more promising.
The main difference between genetic algorithms and stochastic beam search is that genetic algorithms use a population of solutions, while stochastic beam search uses a single solution. Genetic algorithms also use crossover and mutation operators to generate new solutions, while stochastic beam search does not.
In conclusion, the answer to the question “A genetic algorithm (or GA) is a variant of stochastic beam search in which successor states are generated by combining two parent states, rather than by modifying a single state” is False.