ارائه يک مدل جديد ممتيکي مبتني بر اتوماتاي يادگير ساختار ثابت
محورهای موضوعی : مهندسی برق و کامپیوترمهدي رضاپور ميرصالح 1 , محمدرضا میبدی 2
1 - دانشگاه پیام نور تهران
2 - دانشگاه صنعتی امیرکبیر
کلید واژه: الگوريتم ممتيک مم جستجوي محليجستجوي عمومياتوماتاي يادگيراتوماتاي مهاجرت اشيا,
چکیده مقاله :
الگوريتم ممتيک يکی از انواع الگوريتم‌هاي تکاملي است که با استفاده از جستجوي عمومي و جستجوي محلي فضاي حل مسأله را به صورت بهينه جستجو مي‌نمايد. تعادل بين جستجوي عمومي و محلي، همواره يکی از مسايل مهم در اين دسته از الگوريتم‌ها است. در اين مقاله يک مدل جديد ممتيکي با نام 2GALA ارائه شده است. اين مدل از ترکيب الگوريتم ژنتيک و اتوماتاي مهاجرت اشيا که نوع خاصي از اتوماتاي يادگير ساختار ثابت می‌باشد، تشکيل شده است. در مدل ارائه‌شده جستجوي عمومي توسط الگوريتم ژنتيک و يادگيري محلي به وسيله اتوماتاي يادگير انجام مي‌شود. در اين مدل جهت افزايش سرعت همگرايي و فرار از همگرايي زودرس، به طور هم‌زمان از دو مدل يادگيري لامارکي و بالدويني استفاده شده است. در اين مدل تکاملي، جهت استفاده توأم از اثرات مثبت تکامل و يادگيري محلي، کروموزم‌ها به وسيله اتوماتاي مهاجرت اشيا بازنمايي شده‌اند. جهت نمایش برتری مدل ارائه‌شده نسبت به سایر روش‌های موجود، از این مدل برای حل مسأله تناظر گراف استفاده گردیده است.
Memetic algorithm (MA) is a kind of evolutionary algorithms (EAs) that searches the problem solving space using local search and global search. The balance between global search and local search is one of the key issues in this algorithm. In this paper a new model is proposed, called GALA2. This model is combined of genetic algorithm (GA) and object migration automata (OMA), which is a kind of fixed-structure learning automaton. In the proposed model, global search is performed by genetic algorithm and local learning is performed by learning automata. In this model, the Lamarckian and Baldwinian learning models have been used to increase the convergence rate and avoidance of premature convergence, simultaneously. In this evolutionary model, chromosomes are represented by object migration automata for the purpose of using positive effects of evolution and local learning. In order to show the superiority of the proposed model, GALA2 is used to solve the graph isomorphism problem.
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