Evolutionary Multi-Objective Optimization for DNA Sequence Design

Evolutionary Multi-Objective Optimization for DNA Sequence Design

Soo-Yong Shin (Seoul National University, Korea), In-Hee Lee (Seoul National University, Korea) and Byoung-Tak Zhang (Seoul National University, Korea)
DOI: 10.4018/978-1-59904-498-9.ch009
OnDemand PDF Download:


Finding reliable and efficient DNA sequences is one of the most important tasks for successful DNArelated experiments such as DNA computing, DNA nano-assembly, DNA microarrays and polymerase chain reaction. Sequence design involves a number of heterogeneous and conflicting design criteria. Also, it is proven as a class of NP problems. These suggest that multi-objective evolutionary algorithms (MOEAs) are actually good candidates for DNA sequence optimization. In addition, the characteristics of MOEAs including simple addition/deletion of objectives and easy incorporation of various existing tools and human knowledge into the final decision process could increase the reliability of final DNA sequence set. In this chapter, we review multi-objective evolutionary approaches to DNA sequence design. In particular, we analyze the performance of e-multi-objective evolutionary algorithms on three DNA sequence design problems and validate the results by showing superior performance to previous techniques.

Complete Chapter List

Search this Book:
Reviewer List
Table of Contents
Zbigniew Michalewicz
Lam Thu Bui, Sameer Alam
Chapter 1
Lam Thu Bui, Sameer Alam
This chapter is devoted to summarize common concepts related to multi-objective optimization (MO). An overview of “traditional” as well as CI-based... Sample PDF
An Introduction to Multi-Objective Optimization
Chapter 2
Konstantinos E. Parsopoulos, Michael N. Vrahatis
The multiple criteria nature of most real world problems has boosted research on multi-objective algorithms that can tackle such problems... Sample PDF
Multi-Objective Particles Swarm Optimization Approaches
Chapter 3
Saku Kukkonen, Lampinen Jouni
Multi-objective optimization with Evolutionary Algorithms has been gaining popularity recently because its applicability in practical problems. Many... Sample PDF
Generalized Differential Evolution for Constrained Multi-Objective Optimization
Chapter 4
Luis V. Santana-Quintero, Noel Ramírez-Santiago, Carlos A. Coello Coello
This chapter presents a hybrid between a particle swarm optimization (PSO) approach and scatter search. The main motivation for developing this... Sample PDF
Towards a More Efficient Multi-Objective Particle Swarm Optimizer
Chapter 5
Licheng Jiao, Maoguo Gong, Wenping Ma, Ronghua Shang
The human immune system (HIS) is a highly evolved, parallel and distributed adaptive system. The information processing abilities of HIS provide... Sample PDF
Multi-Objective Optimization Using Artificial Immune Systems
Chapter 6
Seamus M. McGovern, Surendra M. Gupta
NP-complete combinatorial problems often necessitate the use of near-optimal solution techniques including heuristics and metaheuristics. The... Sample PDF
Lexicographic Goal Programming and Assessment Tools for a Combinatorial Production Problem
Chapter 7
Andrew Lewis, Sanaz Mostaghim, Marcus Randall
Problems for which many objective functions are to be simultaneously optimised are widely encountered in science and industry. These multi-objective... Sample PDF
Evolutionary Population Dynamics and Multi-Objective Optimisation Problems
Chapter 8
Ramesh Rajagopalan, Chilukuri K. Mohan, Kishan G. Mehrotra, Pramod K. Varshney
Many sensor network design problems are characterized by the need to optimize multiple conflicting objectives. However, existing approaches... Sample PDF
Multi-Objective Evolutionary Algorithms for Sensor Network Design
Chapter 9
Soo-Yong Shin, In-Hee Lee, Byoung-Tak Zhang
Finding reliable and efficient DNA sequences is one of the most important tasks for successful DNArelated experiments such as DNA computing, DNA... Sample PDF
Evolutionary Multi-Objective Optimization for DNA Sequence Design
Chapter 10
Giuseppe Ascia, Vincenzo Catania, Alessandro G. Di Nuovo, Maurizio Palesi, Davide Patti
Multi-Objective Evolutionary Algorithms (MOEAs) have received increasing interest in industry, because they have proved to be powerful optimizers.... Sample PDF
Computational Intelligence to Speed-Up Multi-Objective Design Space Exploration of Embedded Systems
Chapter 11
Jason Teo, Lynnie D. Neri, Minh H. Nguyen, Hussein A. Abbass
This chapter will demonstrate the various robotics applications that can be achieved using evolutionary multi-objective optimization (EMO)... Sample PDF
Walking with EMO: Multi-Objective Robotics for Evolving Two, Four, and Six-Legged Locomotion
Chapter 12
Andrea Toffolo
The research field on energy conversion systems presents a large variety of multi-objective optimization problems that can be solved taking full... Sample PDF
Evolutionary Multi-Objective Optimization in Energy Conversion Systems: From Component Detail to System Configuration
Chapter 13
Mark P. Kleeman, Gary B. Lamont
Assignment problems are used throughout many research disciplines. Most assignment problems in the literature have focused on solving a single... Sample PDF
Evolutionary Multi-Objective Optimization for Assignment Problems
Chapter 14
Mark P. Kleeman, Gary B. Lamont
Evolutionary methods are used in many fields to solve multi-objective optimization problems. Military problems are no exception. This chapter looks... Sample PDF
Evolutionary Multi-Objective Optimization in Military Applications
About the Contributors