人工智能中的局部搜索算法

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人工智能中的局部搜索算法

http://www.cnblogs.com/bgmind/p/4298165.html

在局部搜索算法中,我们不再关心从初始节点到目标节点之间的路径,而是考虑从当前节点出发,移动到它的邻近状态,直到到达合理的目标状态。相比于前面所说的无信息搜索算法和有信息搜索算法,局部搜索算法往往能以常数的空间复杂度(不用保存路径)在很大甚至无限的状态空间中找到合理解。

爬山法

爬山法不断向值增加的方向移动,直到到达顶峰。

function HillClimbing(problem) returns a local maximum state    current_state = initial_state    loop do    next_state = the highest neighbor        if (next_state is higher than current_state)           current_state = next_state        else           return current_state

爬山法的问题在于它只能保证到达局部最大值,却不能保证到达全局最大值。

比如我们从C点出发,那么我们会停在局部最大值A点,因此没办法到达全局最大值B点。

模拟退火算法

  模拟退火算法与爬山法类似,只是我们不再一味地往值增加的方向移动,而是以一定的几率容许往值减小的方向移动,从而使得我们有可能从局部最大值A点走出来,并到达全局最大值B点。
  只所以叫做模拟退火,是因为一开始这个几率相对较高,而随着时间的增加,这个几率则像温度一样慢慢减小。

function SimulatedAnnealing () returns a solution state    current_state = initial_state    for t = 1 to infinite do    T = schedule(t)        if T = 0 then            return current_state        next_state = a randomly selected neighbor        E = next_state.height - current_state.height        if E > 0 then            current_state = next_state        else             current_state = next_state with probability e^(E/T)

遗传算法

  遗传算法模拟生物中的遗传过程,从初始种群开始,迭代进行一系列杂交和变异直到获得合适的种群,并从中挑选出最佳个体。

function GeneticAlgorithm(population, fitin) returns a solution state    inputs: population, a set of individuals            fitness, a function that measures fitness of an individual        repeat    new_population = empty_set        for i = 1 to sizeof(population) do            x = RandomSelect(population, fitness)            y = RandomSelect(population, fitness)            new_individual = Reproduce(x, y)            if (a probability) then            new_individual = Mutate(new_individual)            add new_individual to new_population    until some individuals are fit enough or time has elapsed    return the best individual in the population----------------------------------------------------------------function Reproduce(x, y) returns a new individual    inputs: x, y, the parents of the new individual        length = Length(x)    mutation_point = RandomSelectIn(1, length)    new_individual = Sub(x, 1, mutation_point)                 + Sub(y, mutation_point, length)    return new_individual
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