Thesis Structure (Working Outline)

Chapter 1: Introduction

Multi-objective Combinatorial Optimization

Permutation-Based Representation

Chapter 2: Motivation

Schema Theorem

POP and BBs

Negative Knowledge

Negative Schema

Opposition based learning

Chapter 3: Combinatorial Optimization

Population-Based Method

Genetics Algorithm

Permutation-Based Recombination Operator

Monte Carlo Method

Ant Colony Optimization

Estimation of Distribution Algorithm

Edge Histogram Based Sampling Algorithm

Node Histogram Based Sampling Algorithm

Local Search Method

Simulated Annealing

Tabu Search

Hybrid Algorithms

Chapter 4:Multi-objective Combinatorial Optimization

NSGA

NSGA-II

SPEA

SPEA2

Chapter 5: Methodology

Coincidence Algorithms

Chapter 6: Single Objective

6.1 Single Model

6.2 Multimodal

Chapter 7: Multi Objective

Chapter 8: Conclusion