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

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.

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Structural failures diagnosis using a hybrid artificial intelligence method / Diagnóstico de falhas estruturais utilizando um método híbrido de inteligência artificial

Brazilian Journal of Development ◽

10.34117/bjdv7n7-116 ◽

2021 ◽

Vol 7 (7) ◽

pp. 66873-66893

Author(s):

Simone Silva Frutuoso de Souza ◽

Mailon Bruno Pedri de Campos ◽

Fábio Roberto Chavarette ◽

Fernando Parra dos Anjos Lima

Keyword(s):

Immune System ◽

Negative Selection ◽

Artificial Immune System ◽

Artificial Immune ◽

Aircraft Structure ◽

Inspection Process ◽

Artificial Immune System Algorithm ◽

Structural Failures ◽

Aluminum Beam ◽

Hybrid Artificial Intelligence

This paper presents a Wavelet-artificial immune system algorithm to diagnose failures in aeronautical structures. Basically, after obtaining the vibration signals in the structure, is used the wavelet module for transformed the signals into the wavelet domain. Afterward, a negative selection artificial immune system realizes the diagnosis, identifying and classifying the failures. The main application of this methodology is the auxiliary structures inspection process in order to identify and characterize the flaws, as well as perform the decisions aiming at avoiding accidents or disasters. In order to evaluate this methodology, we carried out the modeling and simulation of signals from a numerical model of an aluminum beam, representing an aircraft structure such as a wing. The results demonstrate the robustness and accuracy methodology.

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A Path-Oriented Test Data Generation Approach Hybridizing Genetic Algorithm and Artificial Immune System

Advances in Intelligent Systems and Computing - Computational Intelligence in Data Mining ◽

10.1007/978-981-10-8055-5_58 ◽

2018 ◽

pp. 649-658

Author(s):

Gargi Bhattacharjee ◽

Ashish Singh Saluja

Keyword(s):

Genetic Algorithm ◽

Immune System ◽

Test Data ◽

Artificial Immune System ◽

Test Data Generation ◽

Data Generation ◽

Artificial Immune

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

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1243/09544062jmes1313 ◽

2009 ◽

Vol 223 (5) ◽

pp. 1243-1252 ◽

Cited By ~ 3

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.

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