Matrix Differential Calculus with Applications in Statistics and Econometrics

Swarm intelligence (SI) is briefly defined as the collective behaviour of decentralized and self-organized swarms. The well known examples for these swarms are bird flocks, fish schools and the colony of social insects such as termites, ants and bees. In 1990s, especially two approaches based on ant colony and on fish schooling/bird flocking introduced have highly attracted the interest of researchers. Although the self-organization features are required by SI are strongly and clearly seen in honey bee colonies, unfortunately the researchers have recently started to be interested in the behaviour of these swarm systems to describe new intelligent approaches, especially from the beginning of 2000s. During a decade, several algorithms have been developed depending on different intelligent behaviours of honey bee swarms. Among those, artificial bee colony (ABC) is the one which has been most widely studied on and applied to solve the real world problems, so far. Day by day the number of researchers being interested in ABC algorithm increases rapidly. This work presents a comprehensive survey of the advances with ABC and its applications. It is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.

A comprehensive survey: artificial bee colony (ABC) algorithm and applications

EvoCOMNET Contributions.- New Research in Nature Inspired Algorithms for Mobility Management in GSM Networks.- Adaptive Local Search for a New Military Frequency Hopping Planning Problem.- SS vs PBIL to Solve a Real-World Frequency Assignment Problem in GSM Networks.- Reconstruction of Networks from Their Betweenness Centrality.- A Self-learning Optimization Technique for Topology Design of Computer Networks.- A Comparative Study of Fuzzy Inference Systems, Neural Networks and Adaptive Neuro Fuzzy Inference Systems for Portscan Detection.- EvoFIN Contributions.- Evolutionary Single-Position Automated Trading.- Genetic Programming in Statistical Arbitrage.- Evolutionary System for Generating Investment Strategies.- Horizontal Generalization Properties of Fuzzy Rule-Based Trading Models.- Particle Swarm Optimization for Tackling Continuous Review Inventory Models.- Option Model Calibration Using a Bacterial Foraging Optimization Algorithm.- A SOM and GP Tool for Reducing the Dimensionality of a Financial Distress Prediction Problem.- Quantum-Inspired Evolutionary Algorithms for Financial Data Analysis.- EvoHOT Contributions.- Analysis of Reconfigurable Logic Blocks for Evolvable Digital Architectures.- Analogue Circuit Control through Gene Expression.- Discovering Several Robot Behaviors through Speciation.- Architecture Performance Prediction Using Evolutionary Artificial Neural Networks.- Evolving a Vision-Driven Robot Controller for Real-World Indoor Navigation.- Evolving an Automatic Defect Classification Tool.- Deterministic Test Pattern Generator Design.- An Evolutionary Methodology for Test Generation for Peripheral Cores Via Dynamic FSM Extraction.- Exploiting MOEA to Automatically Geneate Test Programs for Path-Delay Faults in Microprocessors.- EvoIASP Contributions.- Evolutionary Object Detection by Means of Naive Bayes Models Estimation.- An Evolutionary Framework for Colorimetric Characterization of Scanners.- Artificial Creatures for Object Tracking and Segmentation.- Automatic Recognition of Hand Gestures with Differential Evolution.- Optimizing Computed Tomographic Angiography Image Segmentation Using Fitness Based Partitioning.- A GA-Based Feature Selection Algorithm for Remote Sensing Images.- An Evolutionary Approach for Ontology Driven Image Interpretation.- Hybrid Genetic Algorithm Based on Gene Fragment Competition for Polyphonic Music Transcription.- Classification of Seafloor Habitats Using Genetic Programming.- Selecting Local Region Descriptors with a Genetic Algorithm for Real-World Place Recognition.- Object Detection Using Neural Networks and Genetic Programming.- Direct 3D Metric Reconstruction from Multiple Views Using Differential Evolution.- Discrete Tomography Reconstruction through a New Memetic Algorithm.- A Fuzzy Hybrid Method for Image Decomposition Problem.- Triangulation Using Differential Evolution.- Fast Multi-template Matching Using a Particle Swarm Optimization Algorithm for PCB Inspection.- EvoMUSART Contributions.- A Generative Representation for the Evolution of Jazz Solos.- Automatic Invention of Fitness Functions with Application to Scene Generation.- Manipulating Artificial Ecosystems.- Evolved Diffusion Limited Aggregation Compositions.- Scaffolding for Interactively Evolving Novel Drum Tracks for Existing Songs.- AtomSwarm: A Framework for Swarm Improvisation.- Using DNA to Generate 3D Organic Art Forms.- Towards Music Fitness Evaluation with the Hierarchical SOM.- Evolutionary Pointillist Modules: Evolving Assemblages of 3D Objects.- An Artificial-Chemistry Approach to Generating Polyphonic Musical Phrases.- Implicit Fitness Functions for Evolving a Drawing Robot.- Free Flight in Parameter Space: A Dynamic Mapping Strategy for Expressive Free Impro.- Modelling Video Games' Landscapes by Means of Genetic Terrain Programming - A New Approach for Improving Users' Experience.- Virtual Constructive Swarm Compositions and Inspirations.- New-Generation Methods in an Interpolating EC Synthesizer Interface.- Composing Music with Neural Networks and Probabilistic Finite-State Machines.- TransFormer #13: Exploration and Adaptation of Evolution Expressed in a Dynamic Sculpture.- EvoNUM Contributions.- Multiobjective Tuning of Robust PID Controllers Using Evolutionary Algorithms.- Truncation Selection and Gaussian EDA: Bounds for Sustainable Progress in High-Dimensional Spaces.- Scalable Continuous Multiobjective Optimization with a Neural Network-Based Estimation of Distribution Algorithm.- Cumulative Step Length Adaptation for Evolution Strategies Using Negative Recombination Weights.- Computing Surrogate Constraints for Multidimensional Knapsack Problems Using Evolution Strategies.- A Critical Assessment of Some Variants of Particle Swarm Optimization.- An Evolutionary Game-Theoretical Approach to Particle Swarm Optimisation.- A Hybrid Particle Swarm Optimization Algorithm for Function Optimization.- EvoSTOC Contributions.- Memory Based on Abstraction for Dynamic Fitness Functions.- A Memory Enhanced Evolutionary Algorithm for Dynamic Scheduling Problems.- Compound Particle Swarm Optimization in Dynamic Environments.- An Evolutionary Algorithm for Adaptive Online Services in Dynamic Environment.- EvoTHEORY Contributions.- A Study of Some Implications of the No Free Lunch Theorem.- Negative Slope Coefficient and the Difficulty of Random 3-SAT Instances.- EvoTRANSLOG Contributions.- A Memetic Algorithm for the Team Orienteering Problem.- Decentralized Evolutionary Optimization Approach to the p-Median Problem.- Genetic Computation of Road Network Design and Pricing Stackelberg Games with Multi-class Users.- Constrained Local Search Method for Bus Fleet Scheduling Problem with Multi-depot with Line Change.- Evolutionary System with Precedence Constraints for Ore Harbor Schedule Optimization.

Applications of evolutionary computing: EvoWorkshops 2008: EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog, Naples, Italy, March 26-28, 2008. Proceedings.

[발표] 2015개정 실과 교육과정 ‘가정생활’ 영역 개발 방향

A survey on new generation metaheuristic algorithms

This paper describes a software system for hemodynamic simulation of the blood flow through an aneurysm with rigid walls. Human health significantly depends on the status of blood vessels, however measuring in vivo the important hemodynamic factors in the arterial blood flow, such as wall shear stress, pressure and velocity in blood vessels, is very difficult. Finite Element Mesh (FEM) of the observed blood vessel is a starting point in a patient specific blood flow simulation and measurement of these hemodynamics. A feed-forward neural network is proposed here that performes required computations in each point of the predetermined geometry of the mesh. The architecture of that neural network is two layers with sufficient number of neurons in the hidden layer. Optimal value is empiricaly determined. The first step in the training process is assembling input vectors and the corresponding target vectors, obtained as an output of MedCFD application. Among many variations of backpropagation training functions implemented by generalizing the Widrow-Hoff learning rule, created network object uses Levenberg-Marquardt algorithm. Training continues until the network can associate input vectors with appropriate output vectors. Finally, network is able to simulate and give good responses to new inputs.

http://www.wseas.us/e-library/conferences/2010/Iasi/NNECFS/NNECFS-28.pdf

Neural network based software system for hemodynamic simulation

In this paper we propose a fault model for Programmable Logic Array in which defects appear in programmable elements. The classical models by which the faults in PLA are described (stuck-at-fault, bridging faults and cross point fault model) do not describe the defects which can appear in the programmable elements of PLA. We propose a fault model for PLA in which programmable elements where faults appear get the signal values of permanent logic 1 or permanent logic 0. We also developed a procedure for detecting a multiple fault located at the programmable elements of PLA. The testing procedure makes use of the path sensitization method. It is illustrated by some examples. By the proposed procedure a number of stuck-at-faults, bridging faults and cross point faults are detected as well.

An approach to test determination for programmable logic arrays

The objective of this paper is to develop a model of an image compression, to make simulation and analysis by using Upgraded Petri Net (UPN) This paper describes an Upgraded Petri Net model which can generate results as compressed image for given input data of image and initial marking The UPN presents formal modeling tool, based on mathematical apparatus, used for simulation and analysis of processes, particularly at the register transfer level Original software for modeling and simulations of UPN, PeM (Petri Net Manager), is developed and used for all models described in this paper This paper includes a preface on the UPN theory, the UPN models formulation, their simulation and analysis The first model is a start model of an image compression From this model we can derive a new model which includes elements we want to simulate and analyse The second model can generate data we want to analyse in order to make new initial marking of the model or to change the model The model implements a sub-image JPEG compression by using sequence of split image into blocks, discrete cosine transformation, quantization and entropy encoding By setting the initial state of the model, we can simulate and analyze the different parameters The results are stored into the *mem file inside the PeM The suitability of UPN for modelling of the JPEG image compression is examined and established All models were developed by using original program suite PeM (Petri Net manager)

An upgraded petri net model, simulation and analysis of an 8Ũ8 sub-image for JPEG image compression

In this paper we present an improved algorithm for Petviashvili’s heuristic numerical method for finding solitons in optically induced photonic lattices. Petviashvili’s method is usually used for approximating stationary solutions of nonlinear wave equations to construct numerically the solitary wave solutions such as solitons, lumps, and vortices. We developed and implemented a general software simulator designed for finding solitons in photonic lattices. The Petviashvili’s method is first modified by adding a stabilizing factor that greatly increases stability of the original method. Our software simulator implementation includes a number of criteria for recognizing, at an early stage, divergent or very slowly convergent cases. These criteria, that include Absence of stabilization, Slow convergence, Long interval of instability and Seesaw, significantly lower the overall calculation time.

Optimized Algorithm for Petviashvili's Method for Finding Solitons in Photonic Lattices

Digital images are used in almost every area of the life. The advent of digital images has brought a lot of progress to various scientific areas, but also many new threads have been opened. One of the main problems with digital data and images is unauthorized replication and use. Digital watermarking is one of the techniques for protecting copyrights of digital images by embedding the additional data to indicate the owners of the copyright. In this paper a method for visible digital image watermarking based on compound mapping algorithm is proposed. Compound mapping was used for lossless digital watermarking embedding and removing while security was improved by defining parameters of the compound mapping.

/pdf/visible-digital-imagewatermarking-by-compound-mapping-2fa6jlakfs.pdf

Milan Tuba

Papers

Neural network based software system for hemodynamic simulation

An approach to test determination for programmable logic arrays

An upgraded petri net model, simulation and analysis of an 8Ũ8 sub-image for JPEG image compression

Optimized Algorithm for Petviashvili's Method for Finding Solitons in Photonic Lattices

Visible Digital ImageWatermarking by Compound Mapping Algorithm