Evolutionary Computation Algorithms for Detecting Known and Unknown Attacks

2019 ◽  
pp. 170-184
Author(s):  
Hasanen Alyasiri ◽  
John A. Clark ◽  
Daniel Kudenko
Keyword(s):  
Evolutionary Computation ◽  
Computation Algorithms

Structural optimization of a superlattice infrared photodetector by evolutionary computation algorithms

2019 ◽  
Author(s):  
Rogerio Cortez Brito Leite Povoa ◽  
Pedro Henrique Pereira ◽  
Guilherme M. Torelly ◽  
Douglas Mota Dias ◽  
Germano Maioli Penello ◽  
...  
Keyword(s):  
Structural Optimization ◽  
Evolutionary Computation ◽  
Infrared Photodetector ◽  
Computation Algorithms

scholarly journals Nitrates/nitrites concentration estimation in wastewater samples using transmittance curve models optimized by evolutionary computation techniques

2010 ◽  
Vol 12 (4) ◽  
pp. 446-457
Author(s):  
Pablo López-Espí ◽  
Sancho Salcedo-Sanz ◽  
Ángel M. Pérez-Bellido ◽  
Irene de Bustamante ◽  
Francisco López-Ferreras
Keyword(s):  
Water Sample ◽  
Evolutionary Computation ◽  
Programming Algorithm ◽  
Optimal Parameters ◽  
Nitrite Concentration ◽  
Gaussian Functions ◽  
Concentration Estimation ◽  
Transmittance Curve ◽  
Wastewater Samples ◽  
Computation Algorithms

This paper presents an application of evolutionary computation algorithms in the estimation of the concentration of nitrates/nitrites in water. Specifically, we start from the measured transmittance curves of a water sample and a model of the curve consisting of a mixture of polynomial, Fermi and Gaussian functions. An evolutionary programming algorithm is then used to obtain the optimal parameters of the model which minimize the distance between the measured and the modeled transmittance curves. This process allows us to separate the modeled transmittance curve into several components, one of them associated with the nitrate/nitrite concentration. We can extract the nitrate/nitrite concentration of the water sample using this component of the transmittance model. We test our proposal on several laboratory samples and on three real samples measured in different locations around Madrid, Spain.

scholarly journals A comparative study of particle swarm optimization and genetic algorithm

2019 ◽  
Vol 8 (2) ◽  
pp. 40
Author(s):  
Saman M. Almufti ◽  
Amar Yahya Zebari ◽  
Herman Khalid Omer
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Comparative Study ◽  
Evolutionary Computation ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Computation Algorithms ◽  
Crossover And Mutation

This paper provides an introduction and a comparison of two widely used evolutionary computation algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) based on the previous studies and researches. It describes Genetic Algorithm basic functionalities including various steps such as selection, crossover, and mutation.  

scholarly journals A general framework for statistical performance comparison of evolutionary computation algorithms

2008 ◽  
Vol 178 (14) ◽  
pp. 2870-2879 ◽  
Author(s):  
David Shilane ◽  
Jarno Martikainen ◽  
Sandrine Dudoit ◽  
Seppo J. Ovaska
Keyword(s):  
Evolutionary Computation ◽  
General Framework ◽  
Performance Comparison ◽  
Computation Algorithms
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