Genetic Algorithm | JavaScript 程式專案

{"id":9797,"student_id":1470,"content":"setBackdrop(\"#ffffff\");\n\nvar numOfPeople = 300,\n    mutationRate = 0.3,\n    population = [],\n    geneLen = 5,\n    isMating = false;\n/*\n0 UP\n1 RIGHT\n2 DOWN\n3 LEFT\n*/\n\nvar __creature = {\n            canMove: true,\n            score: 0,\n            x: 100,\n            y: 240,\n            genes: [],\n            initGenes: function () {\n                for (var j = 0; j \u003c geneLen; j++) {\n                    this.genes[j] = Math.floor(Math.random() * 4);\n                }\n            }\n        };\n\n\nfunction initPopulation() {\n\n    for (var i = 0; i \u003c numOfPeople; i++) {\n        var creature = {\n            canMove: true,\n            score: 0,\n            x: 100,\n            y: 240,\n            genes: [],\n            initGenes: function () {\n                for (var j = 0; j \u003c geneLen; j++) {\n                    this.genes[j] = Math.floor(Math.random() * 4);\n                }\n            }\n        };\n\n        creature.initGenes();\n        population[i] = creature;\n\n\n    }\n}\n\ninitPopulation();\n\nfunction drawPopulation() {\n    for (var i = 0; i \u003c numOfPeople; i++) {\n        if(population[i]){\n            pen.drawCircle(population[i].x, population[i].y, 2);\n        }\n        \n    }\n}\n\nfunction evalFitness() {\n    for (var i = 0; i \u003c numOfPeople; i++) {\n        //console.log(\"I: \" + i + \"  \" + population[i]);\n        population[i].score = Math.pow(population[i].x, 1.5);\n    }\n}\n\nfunction getRandElement(arr) {\n    return arr[Math.floor(Math.random() * arr.length)];\n}\n\nfunction mate() {\n    var matingPool = [];\n    var totalFitness = 0;\n    for (var i = 0; i \u003c numOfPeople; i++) {\n        totalFitness += population[i].score;\n\n    }\n    for (var i = 0; i \u003c numOfPeople; i++) {\n\n        var percentFit = Math.ceil(population[i].score*1000/totalFitness);\n        //console.log(population[i].score*1000/totalFitness + \"%\");\n        for (var j = 0; j \u003c percentFit; j++) {\n            matingPool.push(population[i].genes);\n        }\n\n    }\n    // console.log(matingPool);\n    // stop();\n    // //Mating\n    var newPopulation = [];\n    for (var i = 0; i \u003c numOfPeople; i++) {\n        //console.log(getRandElement(matingPool));\n        var a = getRandElement(matingPool);\n        var b = getRandElement(matingPool);\n        \n        var child = __creature;\n        //console.log(child);\n\n        child.canMove = true;\n        var newGenes = [];\n        //console.log(child.genes);\n        for (var j = 0; j \u003c geneLen; j++) {\n            var _rand = Math.random();\n            //console.log(j);\n            if (_rand \u003c 0.5) {\n                child.genes[j] = a[j];\n            } else {\n                child.genes[j] = b[j];\n            }\n            if (_rand \u003c mutationRate) {\n                child.genes[j] = Math.floor(Math.random() * 4); //random gene\n            }\n        }\n        //child.genes = newGenes;\n        newPopulation[i] = child;\n        \n    }\n    population = newPopulation;\n    \n    \n    return newPopulation;\n\n\n\n}\n\nfunction move(step) {\n    for (var i = 0; i \u003c numOfPeople; i++) {\n        \n            if (population[i].canMove) {\n                if (population[i].genes[step] == 0) {\n                    population[i].y -= 1;\n                } else if (population[i].genes[step] == 1) {\n                    population[i].x += 1;\n                } else if (population[i].genes[step] == 2) {\n                    population[i].y += 1;\n                } else if (population[i].genes[step] == 3) {\n                    population[i].x -= 1;\n                }\n            } else if(population[i].x \u003c 0 || population[i].y \u003c 0 || population[i].x \u003e 640 || population[i].y \u003e 480  ){\n                population[i].canMove = false;\n            }\n\n            \n        }\n\n\n}\n\n\nvar step = 0;\nvar generation = 1;\nvar flag = true;\n\n\n\nforever( function(){\nprint(\"Step: \" + step + \", Generation: \" + generation);\n    drawPopulation();    \n    //pen.drawCircle(0,0,100);\n});\n\nsetInterval(function(){\n    if (step \u003c= geneLen \u0026\u0026 !isMating) {\n        //console.log(\"MoVING\")\n\n        if(flag)console.log(population[0].genes[step]+\"  (\"+population[0].x+\",\"+population[0].x+\")\");\n        move(step);\n        if(flag)console.log(\"(\"+population[0].x+\",\"+population[0].x+\")\");\n        //if(generation\u003e1){stop();flag = false;}\n        step++;\n        \n        \n    } else {\n        generation++;\n        evalFitness();\n        isMating = true;\n        population = mate();\n        //console.log(population);\n        //console.log(population.length);\n        generation++;\n        if(flag)console.log(\"Mated\");\n\n        step = 0;\n        isMating = false;\n    }\n}, 100);","created_at":"2017-03-01T20:52:04.729+08:00","updated_at":"2019-10-17T15:55:18.941+08:00","name":"Genetic Algorithm","language":"javascript","screenshot":{"url":"https://cdn2.koding.school/uploads/project/screenshot/9797/f14f7cd43f3ef17bea57bca69a5ca3f1.jpg"},"parent_id":2,"plugin":"","description":null,"note":null,"status":"public","like_student_ids":[],"is_featured":false,"views":455,"hashid":"jzmseezp","is_content_changed":false,"review_status":"unsubmitted","submitted_at":null,"reviewed_at":null,"advise":null,"is_deleted":false}