【发布时间】:2015-10-12 15:08:35
【问题描述】:
在单个项目上成功应用this 遗传算法后,我想优化对象数组的值。
为了做到这一点,我尝试通过以下方式使用 forEach 循环:
function getOptimalValues(req, res){
mdl.getItems({limit: 5}, function(err, items){//this function retrieves 5 items from database
items.forEach(function (elem) {
var queryObj = {
properties: elem,
targetEnergy: req.targetEnergy
};
genetic.getOptimalQuantities(queryObj, function (err, optimalValues) {
geneticResults.push(optimalValues);
if (geneticResults.length == items.length) {
res(null, geneticResults);
}
});
});
});
}
genetic.getOptimalQuantities 定义如下:
function getOptimalQuantities(req, res){
var properties = req.properties;
var targetEnergy = req.targetEnergy;
var targetValues = {energy: targetEnergy, properties: properties};
var Task = require('genetic').Task;
var options = { getRandomSolution : getCandidateQuantities // previously described to produce random solution
, popSize : 100 // population size
, stopCriteria : stopCriteria // previously described to act as stopping criteria for entire process (set to 100 generations)
, fitness : getFitnessValue // previously described to measure how good your solution is
, minimize : false // whether you want to minimize fitness function. default is `false`, so you can omit it
, mutateProbability : 0.1 // mutation chance per single child generation
, mutate : mutate // previously described to implement mutation
, crossoverProbability : 0.3 // crossover chance per single child generation
, crossover : crossoverFunction // previously described to produce child solution by combining two parents
};
var t = new Task(options);
t.targetValues = targetValues;
//t.on('mutate', function () { console.log('MUTATION!') });
t.on('statistics', function (statistics) {
console.log('statistics',statistics.maxScore);
});
t.on('iteration start', function (generation) {
console.log('iteration start - ',generation)
});
t.run(function (stats) {
var dataObj = {quantities: stats.max, items: t.bestCombination}
res(null, dataObj);
});
}
在运行此过程时,我得到以下输出:
iteration start - 1
statistics 0.008126878121533886
iteration start - 1
iteration start - 1
statistics 0.007777620410591467
statistics 0.007777620410591467
iteration start - 1
iteration start - 1
iteration start - 1
statistics 0.008133385505205764
statistics 0.008133385505205764
statistics 0.008133385505205764
iteration start - 1
iteration start - 1
iteration start - 1
iteration start - 1
statistics 0.0093968469349952
statistics 0.0093968469349952
statistics 0.0093968469349952
statistics 0.0093968469349952
iteration start - 1
iteration start - 1
iteration start - 1
iteration start - 1
iteration start - 1
statistics 0.008431076204956763
statistics 0.008431076204956763
statistics 0.008431076204956763
statistics 0.008431076204956763
statistics 0.008431076204956763
iteration start - 2
iteration start - 2
iteration start - 2
iteration start - 2
iteration start - 2
...
对于传递给函数 getOptimalQuantities 的五个项目中的每一个,应该有 100 次迭代,并且每次迭代中每个项目的适应度值(作为统计输出)应该是不同的(所有元素相等的机会接近零)。因此,通过观察输出,我猜遗传算法并没有针对本示例中传递的 5 个项目中的每一个单独运行。
有谁知道如何确保函数(在这种情况下为遗传算法)不会弄乱来自多个输入的数据?直观上,更容易想象每个调用分别顺序执行(这就是要求顺序执行的原因),但总的来说,任何建议的方法,顺序或并行都会非常有帮助。
感谢您的帮助。
尼可
【问题讨论】:
标签: javascript algorithm iteration