scholarly journals Distributed Autonomous Robotic System based on Artificial Immune system and Distributed Genetic Algorithm

2004 ◽  
Vol 14 (2) ◽  
pp. 164-170 ◽  
Author(s):  
Kwee-Bo Sim ◽  
Chul-Min Hwang
Keyword(s):  
Genetic Algorithm ◽  
Immune System ◽  
Artificial Immune System ◽  
Robotic System ◽  
Artificial Immune ◽  
Distributed Genetic Algorithm

A method of multiobjective optimization using a genetic algorithm and an artificial immune system

2009 ◽  
Vol 223 (5) ◽  
pp. 1243-1252 ◽  
Author(s):  
H Park ◽  
N-S Kwak ◽  
J Lee
Keyword(s):  
Genetic Algorithm ◽  
Pattern Recognition ◽  
Immune System ◽  
Artificial Immune System ◽  
Affinity Maturation ◽  
Pareto Optimal ◽  
Mathematical Function ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Artificial Immune

The immune system has pattern recognition capabilities based on reinforced learning, memory, and affinity maturation interacting between antigens (Ags) and antibodies (Abs). This article deals with an adaptation of artificial immune system (AIS) into genetic-algorithm (GA)-based multi-objective optimization. The present study utilizes the pattern recognition from an AIS and the evolution from a GA. Using affinity measures between Ags and Abs, GA-based immune simulation discovers a generalist Ab that represents the common pattern among Ags. Non-dominated Pareto-optimal solutions are obtained via GA-based immune simulation in which dominated designs are considered as Ags, whereas non-dominated designs are assigned to Abs. This article discusses the procedure of identifying Pareto-optimal solutions through the immune system-based pattern recognition. A number of mathematical function problems that are described by discontinuity or disconnection in the shape of Pareto surface are first examined as test examples. Subsequently, engineering optimization problems such as rotating flywheel disc and ten-bar planar truss are explored to support the present study.

Partial imputation of unseen records to improve classification using a hybrid multi-layered artificial immune system and genetic algorithm

2013 ◽  
Vol 13 (12) ◽  
pp. 4461-4480 ◽  
Author(s):  
Mlungisi Duma ◽  
Tshilidzi Marwala ◽  
Bhekisipho Twala ◽  
Fulufhelo Nelwamondo
Keyword(s):  
Genetic Algorithm ◽  
Immune System ◽  
Artificial Immune System ◽  
Artificial Immune

scholarly journals The Evaluated Measurement of a Combined Genetic Algorithm and Artificial Immune System

2017 ◽  
Vol 7 (4) ◽  
pp. 2071
Author(s):  
Pongsarun Boonyopakorn ◽  
Phayung Meesad
Keyword(s):  
Genetic Algorithm ◽  
Immune System ◽  
Negative Selection ◽  
Artificial Immune System ◽  
Optimization Methods ◽  
The Novel ◽  
Artificial Immune ◽  
Input Variables ◽  
Modified Algorithm ◽  
Better Than

This paper demonstrates a hybrid between two optimization methods which are the Artificial Immune System (AIS) and Genetic Algorithm (GA). The novel algorithm called the immune genetic algorithm (IGA), provides improvement to the results that enable GA and AIS to work separately which is the main objective of this hybrid. Negative selection which is one of the techniques in the AIS, was employed to determine the input variables (populations) of the system. In order to illustrate the effectiveness of the IGA, the comparison with a steady-state GA, AIS, and PSO were also investigated. The testing of the performance was conducted by mathematical testing, problems were divided into single and multiple objectives. The five single objectives were then used to test the modified algorithm, the results showed that IGA performed better than all of the other methods. The DTLZ multiobjective testing functions were then used. The result also illustrated that the modified approach still had the best performance.

Sign in / Sign up

Export Citation Format

Share Document