Dynamic choosing mutation and crossover ratios for genetic algorithm

العناوين الأخرى

اختيار نسبة الطفرة و التزاوج ديناميكيا للخوارزمية الجينية

مقدم أطروحة جامعية

Abu Nawas, Iman Yunus

مشرف أطروحة جامعية

al-Hasanat, Ahmad Bashir

أعضاء اللجنة

al-Mahadin, Bassam Muhammad Salim
al-Kasasibah, Muhammad Sharari Zamil
al-Sarayirah, Khalid Turki

الجامعة

جامعة مؤتة

الكلية

كلية تكنولوجيا المعلومات

القسم الأكاديمي

قسم الحاسوب

دولة الجامعة

الأردن

الدرجة العلمية

ماجستير

تاريخ الدرجة العلمية

2016

الملخص الإنجليزي

Genetic Algorithm (GA) is an evolutionary computing algorithm (EC) that is based on evolution and natural selection theory.

It is an efficient tool for solving optimization problems.

Integration among (GA) parameters is vital for successful (GA) search.

Such parameters include mutation and crossover rates in addition to population that are important issues in (GA).

However,each operator of GA has a special and different influence.

The impact of these factors is influenced by their probabilities; it is difficult to predefine specific ratios for each parameter, particularly, mutation and crossover operatorsin this thesis.

In this thesis, new deterministic control approach are proposed for crossover and mutation rates namely: Dynamic Decreasing of high mutation ratio/dynamic increasing of Low Crossover ratio (DHM/ILC), and Dynamic Increasing of Low Mutation/ dynamic Decreasing of High Crossover (ILM/DHC).

The dynamic nature of the proposed methods allows the ratios of both crossover and mutation operators to be changed linearly during the search progress where (DHM/ILC) started with 100% ratio for mutations, and 0% for crossovers.

Both mutation and crossover ratios started to decrease and increase respectively.

By the end of the search process, the ratios will be 0% for mutations and 100% for crossovers.

(ILM/DHC) worked the same but the other way around.

The proposed approachwere compared with two parameters tuning methods (predefined) namely: fifty-fifty crossover/mutation ratios, and the well-known approachthat used common ratios with (0.03) mutation rates and (0.9) crossover rates.

The experiments were conducted on ten problems from Travelling Salesman Problems (TSP).

The experiments showed the effectiveness of the proposed (DHM/ILC) when dealing with small population size, while the proposed (ILM/DHC) was more effective when using large population size.

In fact, both of the proposed dynamic methods outperformed the predefined methods including the literature’s most used common ratios.

التخصصات الرئيسية

تكنولوجيا المعلومات وعلم الحاسوب

عدد الصفحات

53

قائمة المحتويات

• APA
• MLA
• AMA

لغة النص

الإنجليزية

نوع البيانات

رسائل جامعية

رقم السجل

BIM-731648