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Local search algorithm is heuristic in nature. This means that there is no golden or thumb rule. A state space contains all the possible candidate solutions among which the optimal one is selected. An objective function is provided which decides the feasibility of any specific solution and the optimal solution is selected after several iterations of maximization and minimization of the objective function value.
In case of distribution of solutions the size of the state space increases. So, the number of search steps also increases. So, it requires more time than before. Also the possibility of selecting the optimal solution decreases as the number of candidate solutions increase. So, we can say the distribution of solutions affects the performance of a local search algorithm.
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Explain how the distribution of solutions affects the performance of a local search algorithm.
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