Genetic Algorithm 進階 | JavaScript 程式專案

{"id":27200,"student_id":2591,"content":"// noprotect\n\nvar target = createSprite({\n    costumes: \"target.png\",\n    x: 600,\n    y: 240\n});\nvar record = createSprite({\n    costumes: \"ant.png\",\n    x: 0,\n    y: 240\n});\nvar walls = [];\nfunction createRandWall () {\n    var wall = createSprite(\"rect3.png\");\n    wall.x = Math.random()*640;\n    wall.y = Math.random()*480;\n    wall.direction = Math.random()*360;\n    wall.scale = 0.7;\n    walls.push(wall);\n}\nvar population = [];\nvar children = [];\nvar mutationRate = 0.01;\nvar size = 100;\nvar interval = 10;\nvar t = 0;\n\nfor (var i = 0; i \u003c 10; i++) createRandWall();\n\nwhile (population.length \u003c size) {\n    randomDNA();\n}\n\nfunction randomDNA () {\n    let ant = createSprite(\"ant.png\");\n    ant.x = 0;\n    ant.y = 240;\n    ant['path'] = \"\";\n    for (var k = 0; k \u003c 5000; k++) {\n        ant.path += Math.floor(Math.random() * 3 + 1);\n    }\n    population.push(ant);\n}\n\nfunction fitness () {\n    population.sort(function(a, b) {\n        var da = a.distanceTo(target);\n        var db = b.distanceTo(target);\n        return da - db;\n    });\n}\nfunction matePool () {\n    fitness();\n    while (children.length \u003c size) {\n        crossover(population[0], population[0]);\n    }\n    for (var i = 0; i \u003c children.length; i++) {\n        if (population[i].distanceTo(target) \u003c record.distanceTo(target)) {\n            record.x = population[i].x;\n            record.y = population[i].y;\n        }\n        if (record.distanceTo(target) \u003c 20) {\n            stopRendering();\n        }\n        population[i].x = 0;\n        population[i].y = 240;\n        population[i].path = children[i];\n        population[i].hidden = false;\n        population[i].direction = 90;\n    }\n    children = [];\n}\n\nfunction crossover (a, b) {\n    var c = \"\"\n    var mid = Math.floor(Math.random()*key.length);\n    for (var i = 0; i \u003c a.path.length; i++) {\n        if (i \u003c mid) {\n            c += a.path[i];\n        } else {\n            c += b.path[i];\n        }\n        if (Math.random() \u003c mutationRate) {\n            c = c.substr(0, i) + Math.floor(Math.random() * 4 + 1) + c.substr(i + 1);\n        }\n    }\n    children.push(c);\n}\nvar movement = function() {\n    for (var i = 0; i \u003c population.length; i++) {\n        if (population[i].hidden === false) {\n            if (population[i].path[t] == 2) {\n                population[i].direction += 5;\n            } else if (population[i].path[t] == 3) {\n                population[i].direction -= 5;\n            }\n            population[i].stepForward(2);\n            if (population[i].hidden === false) {\n                if (population[i].touched(walls)) {\n                    population[i].hidden = true;\n                }\n            }\n        }\n    }\n    t++;\n    if (t === 350) {\n        t = 0;\n        matePool();\n    }\n    setTimeout(movement, interval);\n};\nsetTimeout(movement, 0);","created_at":"2018-04-27T12:16:33.458+08:00","updated_at":"2019-10-17T09:59:29.010+08:00","name":"Genetic Algorithm 進階","language":"javascript","screenshot":{"url":"https://cdn7.koding.school/uploads/project/screenshot/27200/b0330e5cabaee0c36720ea8dd2902604.jpg"},"parent_id":2,"plugin":"","description":null,"note":null,"status":"public","like_student_ids":[],"is_featured":false,"views":117,"hashid":"9ygsnwjz","is_content_changed":false,"review_status":"unsubmitted","submitted_at":null,"reviewed_at":null,"advise":null,"is_deleted":false}

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