ECTA 2013 Abstracts

Full Papers Short Papers
Full Papers
Paper Nr: 2
Title:

A New Modified Hough Transform Method for Circle Detection
Authors:

A. Oualid Djekoune, Khadija Messaoudi and Mahmoud Belhocine
Abstract: The Hough transform is a powerful tool in image analysis, e.g. circle detection is a fundamental issue in image processing applications of industrial parts or tools. Because of its drawbacks, various modifications of the basic circle Hough transform (CHT) method have been suggested. This paper presents a modified method based on the basic CHT algorithm and using no trigonometric calculations in order to improve the computational performance of the voting process for a good accuracy and robustness of circle detection in a binary image. This paper also provides the errors analysis of the proposed method against the basic CHT method to illustrate that it can replace the basic CHT method for small values of the resolution ε of the angle θ. It then compete the CORDIC algorithm when it is used in a hardware implementation.
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Paper Nr: 11
Title:

A New Methodology for Mitigation of Lava Flow Invasion Hazard - Morphological Evolution of Protective Works by Parallel Genetic Algorithms
Authors:

G. Filippone, W. Spataro, D. D'Ambrosio and D. Marocco
Abstract: The determination of areas exposed to be interested by new eruptive events in volcanic regions is crucial for diminishing consequences in terms of human causalities and damages of material properties. Nevertheless, urbanized areas, cultural heritage sites or even important infrastructures, such as power plants, hospitals and schools can be protected by diverting the flow towards lower interest regions. This paper describes the application of Parallel Genetic Algorithms for optimizing earth barriers construction by morphological evolution, to divert a case study lava flow that is simulated by the numerical Cellular Automata model Sciara-fv2 at Mt Etna (Sicily, Italy). In particular, the application regards the optimization of the position, orientation and extension of an earth barrier built to protect Rifugio Sapienza, a touristic facility located near the summit of the volcano. The study has produced extremely positive results and represents, to our knowledge, the first application of morphological evolution for lava flow mitigation. Among different alternatives generated by the Genetic Algorithm, an interesting scenario consists of an earthen barrier solution (with a length of 225 m, average height of 25 m, base width of 10 m and volume of 56180 m$^{3}$) which completely deviates the flow avoiding that the lava reaches the inhabited area.
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Paper Nr: 24
Title:

Model Complexity Control in Straight Line Program Genetic Programming
Authors:

César L. Alonso, José Luis Montaña and Cruz Enrique Borges
Abstract: In this paper we propose a tool for controlling the complexity of Genetic Programming models. The tool is supported by the theory of Vapnik-Chervonekis dimension (VCD) and is combined with a novel representation of models named straight line program. Experimental results, implemented on conventional algebraic structures (such as polynomials) and real problems, show that the empirical risk, penalized by suitable upper bounds for the Vapnik-Chervonenkis dimension, gives a generalization error smaller than the use of statistical conventional techniques such as Bayesian or Akaike information criteria.
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Paper Nr: 26
Title:

Analysis of Co-evolutionary Approach for Robotic Gait Generation
Authors:

Jan Černý and Jiří Kubalík
Abstract: Recently, a new co-evolutionary approach for generating motion patterns for multi-legged robots which exhibit symmetry and module repetition was proposed. The algorithm consists of two evolutionary algorithms working in co-evolution. The first one, a genetic programming module, evolves a motion of a single leg. The second one, a genetic algorithm module, seeks for the optimal deployment of the single-leg motion pattern to all legs of the robot. Thus, the whole task is decomposed into two subtasks that are to be solved simultaneously. First proof-of-concept experiments proved such a decomposition helps to produce better solutions than a simple GP-based approach that tries to evolve individual motion patterns for all legs of the robot. This paper further analyzes the co-evolutionary algorithm focusing on two things – the way it handles the problem decomposition and the type of functions it uses to control joints of the robot. The experiments carried out in this work indicate that both design choices positively contribute to its performance.
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Paper Nr: 30
Title:

Performance and Scalability of Particle Swarms with Dynamic and Partially Connected Grid Topologies
Authors:

Carlos M. Fernandes, Agostinho C. Rosa, Juan L. J. Laredo, Carlos Cotta and J. J. Merelo
Abstract: This paper investigates the performance and the scalability of dynamic and partially connected 2-dimensional topologies for Particle Swarms, using von Neumann and Moore neighborhoods. The particles are positioned on 2-dimensional grids of nodes, where they move randomly. The von Neumann or Moore neighborhood is used to decide which particles influence each individual. Structures with growing size are tested on a classical benchmark and compared to the lbest, gbest and the standard von Neumann and Moore configurations. The results show that the partially connected grids with von Neumann neighborhood structure perform more consistently than the other strategies, while the Moore partially connected structure performs similarly to the standard Moore configuration. Furthermore, the proposed structure scales similarly or better than the standard configuration when the problem size grows.
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Paper Nr: 32
Title:

A PSO/Snake Hybrid Algorithm for Determining Differential Rotation of Coronal Bright Points
Authors:

E. Shahamatnia, I. Dorotovic, R. A. Ribeiro and J. M. Fonseca
Abstract: Particle swarm optimization (PSO) algorithm is a successful general problem solver, thanks to its computationally inexpensive mechanisms. On the other hand, snake model is a specialized image processing algorithm widely used in applications such as boundary delineation, image segmentation, and object tracking. In this paper we discuss the suitability of a hybrid PSO/Snake algorithm for determining the differential rotation of the Sun’s coronal bright points. In the Snake/PSO hybrid algorithm each particle in the population represents only a portion of the solution and the whole population altogether will converge to the final complete solution. In this model a one-to-one relation between Snake model snaxels and PSO particles have been created and PSO’s evolution equations have been modified with snake model concepts. This hybrid model is tested for tracking the coronal bright points (CBPs) along time, on a set of full-disc solar images obtained with the Atmospheric Imaging Assembly (AIA) instrument, onboard the Solar Dynamics Observatory (SDO) satellite. The algorithm results are then used for determining the differential rotation of CBPs. These final results are compared with those already reported in the literature, to assess the versatility of the PSO/Snake hybrid approach.
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Short Papers
Paper Nr: 3
Title:

Incorporating User Preferences in Many-Objective Optimization using Relation Epsilon-Preferred
Authors:

Nicole Drechsler, Andre Sülflow and Rolf Drechsler
Abstract: During the last 10 years, many-objective optimization problems, i.e. optimization problems with more than three objectives, are getting more and more important in the area of multi-objective optimization. Many real- world optimization problems consist of more than three mutually dependent subproblems, that have to be considered in parallel. Furthermore, the objectives have different levels of importance. For this, priorities have to be assigned to the objectives. In this paper we present a new model for many-objective optimization called Prio-ε-Preferred, where the objectives can have different levels of priorities or user preferences. This relation is used for ranking a set of solutions such that an ordering of the solutions is determined. Prio-ε- Preferred is controlled by a parameter ε, that is problem specific and has to be adjusted experimentally by the designer. Therefore, we also present an extension called Adapted-ε-Preferred (AEP), that determines the ε values automatically without any user interaction. To demonstrate the efficiency of our approach, experiments are performed.
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Paper Nr: 5
Title:

EvoGUITest – A Graphical User Interface Testing Framework based on Evolutionary Algorithms
Authors:

Gentiana Ioana Latiu, Octavian Creţ and Lucia Văcariu
Abstract: Software testing has become an important phase in software applications’ lifecycle. Graphical User Interface (GUI) components can be found in a large number of desktops and web applications and also in a wide variety of systems like mobile phones. In the last years GUIs have become more and more complex and interactive. The GUI testing process requires interaction with the GUI components, mainly by generating mouse and keyboard events. Given their increased importance, GUIs verification for correctness can contribute to the establishment of the correct functionality of the corresponding software application. Most of the current GUI testing methodologies are ad hoc and manual, therefore they are resource consuming. This paper presents EvoGUITest, a novel GUI testing framework based on evolutionary algorithms which tests the GUI independently from the application code itself. EvoGUITest framework is designed for testing GUIs of web applications.
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Paper Nr: 15
Title:

A Modification of Training and Recognition Algorithms for Recognition of Abnormal Behavior of Dynamic Systems
Authors:

Victor Shcherbinin and Valeriy Kostenko
Abstract: We consider the problem of automatic construction of algorithms for recognition of abnormal behavior segments in phase trajectories of dynamic systems. The recognition algorithm is constructed using a set of examples of normal and abnormal behavior of the system. We use axiomatic approach to abnormal behavior recognition to construct abnormal behavior recognizers. In this paper we propose a modification of the genetic recognizer construction algorithm and a novel DTW-based recognition algorithm within this approach. The proposed modification reduces search space for the training algorithm and gives the recognition algorithm more information about phase trajectories. Results of experimental evaluation show that the proposed modification allows to reduce the number of recognition errors by an order of magnitude and to reduce the training time by a factor of 2 in comparison to the existing recognizer and recognizer construction algorithm.
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Paper Nr: 19
Title:

Applying a Hybrid Targeted Estimation of Distribution Algorithm to Feature Selection Problems
Authors:

Geoffrey Neumann and David Cairns
Abstract: This paper presents the results of applying the hybrid Targeted Estimation of Distribution Algorithm (TEDA) to feature selection problems with 500 to 20,000 features. TEDA uses parent fitness and features to provide a target for the number of features required for classification and can quickly drive down the size of the selected feature set even when the initial feature set is relatively large. TEDA is a hybrid algorithm that transitions between the selection and crossover approaches of a Genetic Algorithm (GA) and those of an Estimation of Distribution Algorithm (EDA) based on the reliability of the estimated probability distribution.Targeting the number of features in this way has two key benefits. Firstly, it enables TEDA to efficiently find good solutions for cases with very low signal to noise ratios where the majority of available features are not associated with the given classification task. Secondly, due to the tendency of TEDA to select the smallest and most promising initial feature set, it builds compact classifiers that are able to evaluate populations more quickly than other approaches.
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Paper Nr: 27
Title:

The L-Co-R Co-evolutionary Algorithm - A Comparative Analysis in Medium-term Time-series Forecasting Problems
Authors:

E. Parras-Gutierrez, V. M. Rivas and J. J. Merelo
Abstract: This paper presents an experimental study in which the effectiveness of the L-Co-R method is tested. L-Co-R is a co-evolutionary algorithm to time series forecasting that evolves, on one hand, RBFNs building an appropriate architecture of net, and on the other hand, sets of time lags that represents the time series in order to perform the forecasting using, at the same time, its own forecasted values. This coevolutive approach makes possible to divide the main problem into two subproblems where every individual of one population cooperates with the individuals of the other. The goal of this work is to analyze the results obtained by {\metodo} comparing with other methods from the time series forecasting field. For that, 20 time series and 5 different methods found in the literature have been selected, and 3 distinct quality measures have been used to show the results. Finally, a statistical study confirms the good results of L-Co-R in most cases.
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Paper Nr: 28
Title:

Process Mining through Tree Automata
Authors:

Michal R. Przybylek
Abstract: This paper introduces a new approach to mine business processes. We define bi-directional tree languages together with their finite models and show how they represent business processes. Then we propose an evolutionary heuristic based on skeletal algorithms to learn bi-directional tree automata. We show how the heuristic can be used in process mining.
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Paper Nr: 29
Title:

GREEN-PSO: Conserving Function Evaluations in Particle Swarm Optimization
Authors:

Stephen M. Majercik
Abstract: In the Particle Swarm Optimization (PSO) algorithm, the expense of evaluating the objective function can make it difficult, or impossible, to use this approach effectively; reducing the number of necessary function evaluations would make it possible to apply the PSO algorithm more widely. Many function approximation techniques have been developed that address this issue, but an alternative to function approximation is function conservation. We describe GREEN-PSO (GR-PSO), an algorithm that, given a fixed number of function evaluations, conserves those function evaluations by probabilistically choosing a subset of particles smaller than the entire swarm on each iteration and allowing only those particles to perform function evaluations. The "surplus" of function evaluations thus created allows a greater number of particles and/or iterations. In spite of the loss of information resulting from this more parsimonious use of function evaluations, GR-PSO performs as well as, or better than, the standard PSO algorithm on a set of six benchmark functions, both in terms of the rate of error reduction and the quality of the final solution.
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Paper Nr: 38
Title:

Solving the Examination Timetabling Problem with the Shuffled Frog-leaping Algorithm
Authors:

Nuno Leite, Fernando Melício and Agostinho Rosa
Abstract: Shuffled Frog-Leaping Algorithm (SFLA) is a recently proposed memetic meta-heuristic algorithm for solving combinatorial optimisation problems. SFLA has both global and local search capabilities, and great convergence speed towards the global optimum. Compared to a genetic algorithm, the experimental results show an effective reduction of the number of evaluations required to find the global optimal solution. The Examination Timetabling Problem (ETTP) is a complex combinatorial optimisation problem faced by schools and universities every epoch. In this work, we apply the Shuffled Frog-Leaping Algorithm to solve the ETTP. The evolution step of the algorithm, specifically the local exploration in the submemeplex is carefully adapted based on the prototype SFLA. The algorithm was evaluated on the standard Toronto benchmark instances, and the preliminary experimental results obtained are comparable to those produced by state of art algorithms while requiring much less time.
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Paper Nr: 6
Title:

Use of GCF Aesthetic Measure in the Evolution of Landscape Designs
Authors:

Prasad Gade and Paul Walsh
Abstract: This paper explores the use of a global contrast factor (GCF) as an aesthetic measure to aid the generation of fractal landscapes. In an attempt to auto generation virtual landscapes, we added a global contrast factor as an aesthetic measure based fitness function to the genetic algorithm (GA). This GA is used to explore a multi-dimensional parameter space that defines how 3D fractal landscapes are created. Two types of experiments were conducted using GCF that facilitated fluid evaluation of computationally intensive fitness evaluation, with preliminary results reported.
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Paper Nr: 8
Title:

Acquiring Method for Agents’ Actions using Pheromone Communication between Agents
Authors:

Hisayuki Sasaoka
Abstract: We have known that an algorithm of Ant Colony System (ACS) and Max-Min Ant System (MM-AS) based on ACS are one of powerful meta-heuristics algorithms and some researchers have reported their effectiveness of some applications using then. On the other hand, we have known that the algorithms have some problems when we employed them in multi-agent system and we have proposed a new method which is improved MM-AS. This paper describes some results of evaluation experiments with agents implemented our proposed method. In these experiments, we have used seven maps and scenarios for RoboCup Rescue Simulation system (RCRS). To confirm the effectiveness of our method, we have considered agents’ action for fire-fighting in simulation and their improvements of scores.
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Paper Nr: 10
Title:

Chaotic Quantum-behaved Particle Swarm Optimization Approach Applied to Inverse Heat Transfer Problem
Authors:

Leandro dos Santos Coelho, Fabio A. Guerra, Bruno Pasquim and Viviana Cocco Mariani
Abstract: Particle swarm optimization (PSO) algorithms are attracting attentions in recent years, due to their ability of keeping good balance between convergence and diversity maintenance. Several attempts have been made to improve the performance of the original PSO algorithm. Inspired by trajectory analysis of the PSO and quantum mechanics, a quantum-behaved particle swarm optimization (QPSO) algorithm was recently proposed. QPSO has shown some important advantages by providing high speed of convergence in specific problems, but it has a tendency to get stuck in a near optimal solution and one may find it difficult to improve solution accuracy by fine tuning. In this paper, a modified and efficient version of the QPSO combined with chaotic sequences (CQPSO) is proposed and evaluated. We conduct simulations to estimate the unknown variables of an inverse heat transfer problem to verify the performance of the proposed CQPSO method and show that the method can be competitive when compared with the classical QPSO.
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Paper Nr: 16
Title:

Multi-objective Scatter Search with External Archive for Portfolio Optimization
Authors:

Khin Lwin, Rong Qu and Jianhua Zheng
Abstract: The relevant literature showed that many heuristic techniques have been investigated for constrained portfolio optimization problem but none of these studies presents multiobjective Scatter Search approach. In this work, we present a hybrid multiobjective population-based evolutionary algorithm based on Scatter Search with an external archive to solve the constrained portfolio selection problem. We considered the extended mean-variance portfolio model with three practical constraints which limit the number of assets in a portfolio, restrict the proportions of assets held in the portfolio and pre-assign some specific assets in the portfolio. The proposed hybrid metaheuristic algorithm follows the basic structure of the Scatter Search and defines the reference set solutions based on Pareto dominance and crowding distance. New Subset generation and combination methods are proposed to generate efficient and diversified portfolios. Hill Climbing operation is integrated to search for improved portfolios. The performance of the proposed multiobjective Scatter Search algorithm is compared with Non-dominated Sorting Genetic Algorithm (NSGA-II). Experimental results indicate that the proposed algorithm is a promising approach for solving the constrained portfolio selection problem. Measurements by the performance metrics indicate that it outperforms NSGA-II on the solution quality within a shorter computational time.
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Paper Nr: 17
Title:

Enhancing Optimal Weight Tuning in H8 Loop-shaping Control with Particle Swarm Optimizations
Authors:

Philippe Feyel, Gilles Duc and Guillaume Sandou
Abstract: The H loop-shaping controllers have proven their efficiency to solve problems based on complex industrial specifications. However, the design is based on the tuning of weighting filters to reformulate all the specifications, which is a time consuming task requiring know-how and expertise. This paper deals with the use of Particle Swarm Optimization (PSO) algorithm for tuning the weighting filters. Whereas this topic has already been investigated in lots of works especially using evolutionary algorithms, we propose here to enhance the optimization process by working on the definition of a generic fitness function from a general high-level specification, and by relaxing constraints on weights structure. The developed methodology is tested using a real industrial example and leads to satisfactory results.
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Paper Nr: 18
Title:

A Review of Artificial Immune Systems
Authors:

Zafer Ataser
Abstract: Artificial Immune Systems (AIS) are class of computational intelligent methods developed based on the principles and processes of the biological immune system. AIS methods are categorized mainly into four types according to the inspired principles and processes of immune system. These categories are clonal selection, negative selection, immune network and danger theory. This paper reviews the models of AIS and the progress of them. The fundamental characteristics of AIS models are identified and some major studies of each model are given. In addition to that, some application areas of AIS models are explained.
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Paper Nr: 35
Title:

Testing the Differences of using RGB and HSV Histograms during Evolution in Evolutionary Art
Authors:

P. García-Sánchez, J. J. Merelo, D. Calandria, A. B. Pelegrina, R. Morcillo, F. Palacio and R. H. García-Ortega
Abstract: This paper compares the use of RGB and HSV histograms during the execution of an Evolutionary Algorithm. This algorithm generates abstract images that try to match the histograms of a target image. Three different fitness functions have been used to compare: the differences between the individual with the RGB histogram of the test image, the HSV histogram, and an average of the two histograms at the same time. Results show that the HSV fitness also increases the similarities of the RGB (and therefore, the average) more than the other two measures.
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