تطبيق خوارزمية هجينة تجمع بين الخوارزمية الجينية وخوارزمية تسلق التلال في بيئات التعلم القائمة على الميتافيرس
Application of a Hybrid Genetic and Hill Climbing Algorithm (HGA) in Metaverse-Based Learning Environments
DOI:
https://doi.org/10.54153/sjpas.2026.v8i1.1359الكلمات المفتاحية:
Application of a Hybrid Genetic and Hill Climbing Algorithm (HGA) in Metaverse-Based Learning Environmentsالملخص
تُنتج بيئات التعلم القائمة على الميتافيرس كميات هائلة من بيانات الوسائط المتعددة من تفاعلات المتعلمين، بما في ذلك تتبع حركة العين، وأنماط الحركة، والإشارات السمعية والسلوكية. تُشكل هذه البيانات عالية الأبعاد تحديًا حسابيًا يؤثر على سرعة وكفاءة أنظمة التعلم التكيفي. تهدف هذه الدراسة إلى تحسين اختيار الميزات من خلال اقتراح خوارزمية هجينة تجمع بين الخوارزميات الجينية (GA) وخوارزميات تسلق التلال (HCA) لتحقيق توازن فعال بين البحث الدقيق والتحسين المحلي. تُعزز خوارزمية تسلق التلال قدرة الخوارزميات الجينية على تجنب التقارب المبكر نحو الحلول المحلية، كما تُحسّن جودة الميزات المختارة. تم تقييم الخوارزمية المقترحة على ست مجموعات بيانات قياسية، وأظهرت النتائج تحسنًا ملحوظًا في دقة التصنيف باستخدام عدد أقل من الميزات المختارة مقارنةً بالعديد من الخوارزميات المعروفة. في سياق التعلم القائم على الميتافيرس، يُقلل النموذج المقترح من حجم البيانات المُعالجة في الوقت الفعلي، ويُمكّن من الحصول على تغذية راجعة فورية، ويُحسّن كفاءة أنظمة التحليل التكيفي ضمن بيئات التعلم الغامرة.
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