Dynamic choosing mutation and crossover ratios for genetic algorithm

Other Title(s)

اختيار نسبة الطفرة و التزاوج ديناميكيا للخوارزمية الجينية

Dissertant

Abu Nawas, Iman Yunus

Thesis advisor

al-Hasanat, Ahmad Bashir

Comitee Members

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

University

Mutah University

Faculty

Information Technology College

Department

Computer Science Department

University Country

Jordan

Degree

Master

Degree Date

2016

English Abstract

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.

Main Subjects

Information Technology and Computer Science

No. of Pages

53

Table of Contents

• APA
• MLA
• AMA

Language

English

Data Type

Arab Theses

Record ID

BIM-731648