ECTA 2014 Abstracts


Full Papers
Paper Nr: 2
Title:

Algorithms for a Hybrid Flexible Flowshop Problem with Sequence-dependent Setup Times

Authors:

Aymen Sioud, Caroline Gagné and Marc Gravel

Abstract: This paper presents a hybrid genetic algorithm to address the problem of minimizing the makespan in a hybrid flexible flowshop with sequence-dependent setup times. The proposed algorithm embeds an ant colony optimization in a crossover operator. Numerical experiments were performed to compare the performance of the proposed algorithm on different benchmarks from the literature where its compared with the best algorithms from the literature.The results indicate that our algorithm generate better solutions than those of the known reference sets.

Paper Nr: 13
Title:

Evolutionary Tuning of Optimal Controllers for Complex Systems

Authors:

Jesús-Antonio Hernández-Riveros, Jorge-Humberto Urrea-Quintero and Cindy Carmona-Cadavid

Abstract: The Proportional Integral Derivative controller is the most widely used industrial device for monitoring and controlling processes. Although there are alternatives to the traditional rules of tuning, there is not yet a study showing that the use of heuristic algorithms it is indeed better than using the classic methods of optimal tuning. Current trends in controller parameter estimation minimize an integral performance criterion. In this paper, an evolutionary algorithm (MAGO - Multidynamics Algorithm for Global Optimization) is used as a tool to optimize the controller parameters minimizing the ITAE (Integral of Time multiplied by Absolut Error) performance index. The procedure is applied to a set of standard plants modelled as a Second Order System Plus Time Delay (SOSPD). Operating on servo and regulator modes and regardless the plant used, the evolutionary approach gets a better overall performance comparing to traditional methods (Bohl and McAvoy, Minimum ITAE-Hassan, Minimum ITAE-Sung). The solutions obtained cover all restrictions and extends the maximum and minimum boundaries between them.

Paper Nr: 19
Title:

Spectral Clustering Using Evolving Similarity Graphs

Authors:

Christina Chrysouli and Anastasios Tefas

Abstract: In this paper, we propose a novel spectral graph clustering method that uses evolutionary algorithms in order to optimise the structure of a graph, by using a fitness function, applied in clustering problems. Nearest neighbour graphs and variants of these graphs are used in order to form the initial population. These graphs are transformed in such a way so as to play the role of chromosomes in the evolutionary algorithm. Multiple techniques have been examined for the creation of the initial population, since it was observed that it plays an important role in the algorithm's performance. The advantage of our approach is that, although we emphasise in clustering applications, the algorithm may be applied to several other problems that can be modeled as graphs, including dimensionality reduction and classification. Experiments on traditional dance dataset and on other various multidimensional datasets were conducted using both internal and external clustering criteria as evaluation methods, which provided encouraging results.

Paper Nr: 21
Title:

Applications of Genetic Algorithm on Optimal Sequence for Parrondo Games

Authors:

Degang Wu and Kwok Yip Szeto

Abstract: Parrondo game, which introduction is inspired by the flashing Brownian ratchet, presents an apparently paradoxical situation at it shows that there are ways to combine two losing games into a winning one. The original Parrondo game consists of two individual games, game A and game B. Game A is a slightly losing coin-tossing game. Game B has two coins, with an integer parameter $M$. If the current cumulative capital (in discrete unit) is a multiple of $M$, an unfavorable coin $p_b$ is used, otherwise a favorable $p_g$ coin is used. Game B is also a losing game if played alone. Paradoxically, combination of game A and game B could lead to a winning game, either through random mixture, or deterministic switching. In deterministic switching, one plays according to a sequence such as ABABB. Exhaustive search and backward induction have been applied to the search for optimal finite game sequence. In this paper, we apply genetic algorithm (GA) to search for optimal game sequences with a given length $N$ for large $N$. Based on results obtained through a problem-independent GA, we adapt the point mutation operator and one-point crossover operator to exploit the structure of the optimal game sequences. We show by numerical results the adapted problem-dependent GA has great improvement in performance.

Paper Nr: 22
Title:

Learning Multi-tree Classification Models with Ant Colony Optimization

Authors:

Khalid M. Salama and Fernando E. B. Otero

Abstract: Ant Colony Optimization (ACO) is a meta-heuristic for solving combinatorial optimization problems, inspired by the behaviour of biological ant colonies. One of the successful applications of ACO is learning classification models (classifiers). A classifier encodes the relationships between the input attribute values and the values of a class attribute in a given set of labelled cases and it can be used to predict the class value of new unlabelled cases. Decision trees have been widely used as a type of classification model that represent comprehensible knowledge to the user. In this paper, we propose the use of ACO-based algorithms for learning an extended multi-tree classification model, which consists of multiple decision trees, one for each class value. Each class-based decision trees is responsible for discriminating between its class value and all other values available in the class domain. Our proposed algorithms are empirically evaluated against well-known decision trees induction algorithms, as well as the ACO-based Ant-Tree-Miner algorithm. The results show an overall improvement in predictive accuracy over 32 benchmark datasets. We also discuss how the new multi-tree models can provide the user with more understanding and knowledge-interpretability in a given domain.

Paper Nr: 27
Title:

Is it Possible to Generate Good Earthquake Risk Models Using Genetic Algorithms?

Authors:

Claus Aranha, Yuri Cossich Lavinas, Marcelo Ladeira and Bogdan Enescu

Abstract: Understanding the mechanisms and patterns of earthquake occurrence is of crucial importance for assessing and mitigating the seismic risk. In this work we analyze the viability of using Evolutionary Computation (EC) as a means of generating models for the occurrence of earthquakes. Our proposal is made in the context of the "Collaboratory for the Study of Earthquake Predictability" (CSEP), an international effort to standardize the study and testing of earthquake forecasting models. We use a standard Genetic Algorithm (GA) with real valued genome, where each allele corresponds to a bin in the forecast model. The design of an appropriate fitness function is the main challenge for this task, and we describe two different proposals based on the log-likelihood of the candidate model against the training data set. The resulting forecasts are compared with the Relative Intensity algorithm, which is traditionally employed by the CSEP community as a benchmark, using data from the Japan Meteorological Agency (JMA) earthquake catalog. The forecasts generated by the GA were competitive with the benchmarks, specially in scenarios with a large amount of inland seismic activity.

Paper Nr: 32
Title:

Evolutionary Learning of Weighted Linear Composite Dispatching Rules for Scheduling

Authors:

Helga Ingimundardottir and Thomas Philip Runarsson

Abstract: A prevalent approach to solving job shop scheduling problems is to combine several relatively simple dispatching rules such that they may benefit each other for a given problem space. Generally, this is done on an ad-hoc basis, requiring expert knowledge from heuristics designer, or extensive exploration of suitable combinations of heuristics. The approach here, is to automate that selection, by translating dispatching rules into measurable features and optimising what their contribution should be via evolutionary search. The framework is straight forward and easy to implement and shows promising results. Various data distributions are investigated, for both job shop and flow shop problems, as is scalability for higher dimensions. Moreover, the study shows that the choice of objective function for evolutionary search is worth investigating. Since the optimisation is based on minimising the expected mean of the fitness function over a large set of problem instances, which can vary within. Then normalising the objective function can stabilise the optimisation process away from local minima.

Paper Nr: 33
Title:

Occupational Diseases Risk Prediction by Cluster Analysis and Genetic Optimization

Authors:

Antonio Di Noia, Paolo Montanari and Antonello Rizzi

Abstract: This paper faces the health risk prediction problem in workplaces through computational intelligence techniques applied to a set of data collected from the Italian national system of epidemiological surveillance. The goal is to create a tool that can be used by occupational physicians in monitoring visits, as it performs a risk assessment for workers of contracting some particular occupational diseases. The proposed algorithm, based on a clustering technique is applied to a database containing data on occupational diseases collected by the Local Health Authority (ASL) as part of the Surveillance National System. A genetic algorithm is in charge to optimize the classification model. First results are encouraging and suggest interesting research tasks for further systems’ development.

Paper Nr: 43
Title:

Studying and Tackling Noisy Fitness in Evolutionary Design of Game Characters

Authors:

J. J. Merelo, Pedro A. Castillo, Antonio Mora, Antonio Fernández-Ares, Anna I. Esparcia-Alcázar, Carlos Cotta and Nuria Rico

Abstract: In most computer games as in life, the outcome of a match is uncertain due to several reasons: the characters or assets appear in different initial positions or the response of the player, even if programmed, is not deterministic; different matches will yield different scores. That is a problem when optimizing a game-playing engine: its fitness will be noisy, and if we use an evolutionary algorithm it will have to deal with it. This is not straightforward since there is an inherent uncertainty in the true value of the fitness of an individual, or rather whether one chromosome is better than another, thus making it preferable for selection. Several methods based on implicit or explicit average or changes in the selection of individuals for the next generation have been proposed in the past, but they involve a substantial redesign of the algorithm and the software used to solve the problem. In this paper we propose new methods based on incremental computation (memory-based) or fitness average or, additionally, using statistical tests to impose a partial order on the population; this partial order is considered to assign a fitness value to every individual which can be used straightforwardly in any selection function. Tests using several hard combinatorial optimization problems show that, despite an increased computation time with respect to the other methods, both memory-based methods have a higher success rate than implicit averaging methods that do not use memory; however, there is not a clear advantage in success rate or algorithmic terms of one method over the other

Paper Nr: 44
Title:

Particle Swarms with Dynamic Topologies and Conservation of Function Evaluations

Authors:

Carlos M. Fernandes, Juan L. J. Laredo, Juan Julian Merelo, Carlos Cotta and Agostinho Rosa

Abstract: This paper proposes a general framework for structuring dynamic Particle Swarm populations and uses a conservation of function evaluations strategy to increase the convergence speed. The population structure is constructed by placing the particles on a 2-dimensional grid of nodes, where they interact and move according to simple rules. During the running time of the algorithm, the von Neumann neighborhood is used to decide which particles influence each other when updating their velocity and position. Each particle is updated in each time-step but they are evaluated only if there are other particles in their neighborhood. A set of experiments demonstrates that the dynamics imposed by the structure provides a more consistent and stable behavior throughout the test set when compared to standard topologies, while the conservation of evaluations significantly reduces the convergence speed of the algorithm. Furthermore, the working mechanisms of the proposed structure are very simple and, except for the size of the grid, they do not require parameters and tuning.

Paper Nr: 46
Title:

Evolutionary Optimization of a One-Class Classification System for Faults Recognition in Smart Grids

Authors:

Enrico De Santis, Gianluca Distante, Fabio Massimo Frattale Mascioli, Alireza Sadeghian and Antonello Rizzi

Abstract: The Computational Intelligence paradigm has proven to be a useful approach when facing problems related to Smart Grids (SG). The modern SG systems are equipped with Smart Sensors scattered in the real-world power distribution lines that are able to take a fine-grained picture of the actual power grid state gathering a huge amount of heterogeneous data. Modeling and predicting general faults instances by means of processing structured patterns of faults data coming from Smart Sensors is a very challenging task. This paper deals with the problem of faults modeling and recognition on MV feeders in the real-world Smart Grid system that feeds the city of Rome, Italy. The faults recognition problem is faced by means of a One-Class classifier based on a modified k-means algorithm trained through an evolutive approach. Due to the nature of the specific data-driven problem at hand, a custom weighted dissimilarity measure designed to cope with mixed data type like numerical data, Time Series and categorical data is adopted. For the latter a Semantic Distance (SD) is proposed, capable to grasp semantical information from clustered data. A genetic algorithm is in charge to optimize system’s performance. Tests were performed on data gathered over three years by ACEA Distribuzione S.p.A., the company that manages the power grid of Rome.

Paper Nr: 47
Title:

Information Granules Filtering for Inexact Sequential Pattern Mining by Evolutionary Computation

Authors:

Enrico Maiorino, Francesca Possemato, Valerio Modugno and Antonello Rizzi

Abstract: Nowadays, the wide development of techniques to communicate and store information of all kinds has raised the need to find new methods to analyze and interpret big quantities of data. One of the most important problems in sequential data analysis is frequent pattern mining, that consists in finding frequent subsequences (patterns) in a sequence database in order to highlight and to extract interesting knowledge from the data at hand. Usually real-world data is affected by several noise sources and this makes the analysis more hallenging, so that approximate pattern matching methods are required. A common procedure employed to identify recurrent patterns in noisy data is based on clustering algorithms relying on some edit distance between subsequences. When facing inexact mining problems, this plain approach can produce many spurious patterns due to multiple pattern matchings on the same sequence excerpt. In this paper we present a method to overcome this drawback by applying an optimization-based filter that identifies the most descriptive patterns among those found by the clustering process, able to return clusters more compact and easily interpretable. We evaluate the mining system’s performances using synthetic data with variable amounts of noise, showing that the algorithm performs well in synthesizing retrieved patterns with acceptable information loss.

Paper Nr: 69
Title:

A Shuffled Complex Evolution Based Algorithm for Examination Timetabling - Benchmarks and a New Problem Focusing Two Epochs

Authors:

Nuno Leite, Fernando Melício and Agostinho C. Rosa

Abstract: In this work we present a memetic algorithm for solving examination timetabling problems. Two problems are analysed and solved. The first one is the well-studied single-epoch problem. The second problem studied is an extension of the standard problem where two examination epochs are considered, with different durations. The proposed memetic algorithm inherits the population structure of the Shuffled Complex Evolution algorithm, where the population is organized into sets called complexes. These complexes are evolved independently and then shuffled in order to generate the next generation complexes. In order to explore new solutions, a crossover between two complex’s solutions is done. Then, a random solution selected from the top best solutions is improved, by applying a local search step where the Great Deluge algorithm is employed. Experimental evaluation was carried out on the public uncapacitated Toronto benchmarks (single epoch) and on the ISEL-DEETC department examination benchmark (two epochs). Experimental results show that the proposed algorithm is efficient and competitive on the Toronto benchmarks with other algorithms from the literature. Relating the ISEL-DEETC benchmark, the algorithm attains a lower cost when compared with the manual solution.

Paper Nr: 71
Title:

Going a Step Beyond the Black and White Lists for URL Accesses in the Enterprise by Means of Categorical Classifiers

Authors:

A. M. Mora, P. De las Cuevas and J. J. Merelo

Abstract: Corporate systems can be secured using an enormous quantity of methods, and the implementation of Black or White lists is among them. With these lists it is possible to restrict (or to allow) the users the execution of applications or the access to certain URLs, among others. This paper is focused in the latter option. It describes the whole processing of a set of data composed by URL sessions performed by the employees of a company; from the preprocessing stage, including labelling and data balancing processes, to the application of several classification algorithms. The aim is to define a method for automatically make a decision of allowing or denying future URL requests, considering a set of corporate security policies. Thus, this work goes a step beyond the usual black and white lists, since they can only control those URLs that are specifically included in them, but not by making decisions based in similarity (through classification techniques), or even in other variables of the session, as it is proposed here. The results show a set of classification methods which get very good classification percentages (95-97%), and which infer some useful rules based in additional features (rather that just the URL string) related to the user's access. This led us to consider that this kind of tool would be very useful tool for an enterprise.

Short Papers
Paper Nr: 12
Title:

Implementing Parallel Genetic Algorithm Using Concurrent-functional Languages

Authors:

José Albert Cruz, J. J. Merelo, Liesner Acevedo-Martínez and Paloma de las Cuevas

Abstract: The spread of multiprocessor and multi-core architectures have a pervasive effect on the way software is developed. In order to take full advantage of them, a parallel implementation of every single program would be needed, but also a radical reformulation of the algorithms that are more appropriate to that kind of implementation. In this work we design and implement an evolutionary computation model using programming languages with built-in concurrent concepts. This article shows the advantages of these paradigms in order to implement a parallel genetic algorithm (pGA) with an island pools based topology in the concurrent-functional oriented programming languages: Erlang, Scala, and Clojure. Some implementation decisions are analyzed and the results of the solution of a study case are shown.

Paper Nr: 16
Title:

Impatience Mechanism in Saddles' Crossing

Authors:

Iwona Karcz-Duleba

Abstract: Evolutionary inspired heuristics suffer from a premature convergence at local optima and, consequently, a population diversity loss. Thus, breaking out of a local optimum trap and crossing saddles between optima in multimodal and multidimensional search spaces is an important issue in an evolutionary optimization algorithm. In this paper, an impatience mechanism coupled with a phenotypic model of evolution is studied. This mechanism diversifies a population and facilitates escaping from a local optima trap. An impatient population polarizes itself and evolves as a dipole centered around an averaged individual. The operator was modified by supplying it with an extra knowledge about a currently found optimum. In the case, behavior of a population is quite different – a significant diversification is observed but the population is not polarized and evolves as a single cluster. Both mechanisms allow to cross saddle relatively fast for a wide range of parameters of a bimodal multidimensional fitness function.

Paper Nr: 17
Title:

Dynamic Heterogeneous Multi-Population Cultural Algorithm for Large Scale Global Optimization

Authors:

Mohammad R. Raeesi N. and Ziad Kobti

Abstract: Dynamic Heterogeneous Multi-Population Cultural Algorithm (D-HMP-CA) is a novel algorithm to solve global optimization problems. It incorporates a number of local Cultural Algorithms (CAs) and a shared belief space. D-HMP-CA benefits from its dynamic decomposition techniques including the bottom-up and top-down strategies. These techniques divide the problem dimensions into a number of groups which will be assigned to different local CAs. The goal of this article is to evaluate the algorithm scalability. In order to do so, D-HMP-CA is applied on a benchmark of large scale global optimization problems. The results show that the top-down strategy outperforms the bottom-up technique by offering better solutions, while within lower size optimization problems the bottom-up approach presents a better performance. Generally, this evaluation reveals that D-HMP-CA is an efficient method for high dimensional optimization problems due to its computational complexity for both CPU time and memory usage. Furthermore, it is an effective method such that it offers competitive solutions compared to the state-of-the-art methods.

Paper Nr: 23
Title:

Concurrent Optimization of Flight Distance and Robustness of Equipment and Skills in Discus Throwing

Authors:

Kazuya Seo and Kana Takaoka

Abstract: This paper describes the concurrent optimization of flight distance and ‘robustness’ of the equipment and skills in a discus. Two objective functions are considered. One is the flight distance, and the other is robustness. Robustness is defined as insensitivity to deviations from the local optimal release conditions. The aim of the optimization is to maximize both the flight distance and the robustness. Fourteen design variables are considered. Eight of the fourteen are concerned with the skill of the thrower. They determine the launch conditions, which are controlled by the thrower when he or she throws. The other six variables are concerned with the design of the equipment. These are the dimensions of the discus, the moment of inertia about the transverse axis and finally the mass of the discus. The dependences of size and the angle of attack on the aerodynamic data are estimated by using CFD (computational fluid dynamics) technique. It was found that there is a trade-off between flight distance and robustness. The flight distance is 78.8 meters at the sweet spot solution, where both objective functions have better values simultaneously. The stalling angle for the sweet spot solution is relatively high.

Paper Nr: 26
Title:

Altering the Granularity of Neutrality in a Multi-layered Genetic Algorithm

Authors:

Seamus Hill and Colm O’Riordan

Abstract: By adopting a basic interpretation of the biological processes of transcription and translation, the multilayered GA (MGA) introduces a genotype-phenotype mapping for a haploid genotype, which allows the granularity of the representation to be tuned. The paper examines the impact of altering the level of neutrality through changes in the granularity of the representation and compares the performance of a standard GA (SGA) to that of a number of multi-layered GAs, each with a different level of neutrality, over both static and changing environments. Initial results indicate that it appears advantageous to include a multi-layered, biologically motivated genotype-phenotype encoding over more difficult landscapes. The paper also introduces an interpretation of missense mutation, which operates within the genotype-phenotype map (GP-map). Results also suggest that this mutation strategy can assist in tracking the optimum over various landscapes.

Paper Nr: 30
Title:

Iterated Prisoner's Dilemma with Partial Imitation in Noisy Environments

Authors:

Andre Amend, Degang Wu and Kwok Yip Szeto

Abstract: Players with one-step memory in an iterated Prisoner's Dilemma game can adaptively change their strategies after playing some games with their opponent. The probability of change of strategies depends on noise levels, the players’ patience (or reaction time), and initial strategies. Players perform partial imitation, since, realistically, they can only imitate what they observe. Patience determines the frequency of a player's possible strategies changes. In this paper, we focus on the evolution of strategies between two major categories of players whose innate characters belong either to cheaters (traitors) or nice (benevolent) players. We consider them as agents whose characters are fixed, but their detailed genetic makeup can still vary among several types, so that, for example, the cheaters can evolve among different types of cheaters. We observe their evolutions by means of their degree of cooperation, where the variables are initial strategies, noise, and patience. Here, noise is incorporated in a sigmoid function that accounts for errors in learning. The numerical results show interesting features that we can explain heuristically: in the iterated games between an adaptive cheater against a patient nice player in a noisy environment, we observe a minimum degree of cooperation at a specific noise level.

Paper Nr: 31
Title:

Multiprocessor Real-time Scheduling Using an Optimization-based Technique

Authors:

Anca Hangan, Gheorghe Sebestyen and Lucia Vacariu

Abstract: The paper presents an optimization-based technique that enhances the schedulability of real-time transactional multiprocessor systems. The technique addresses two important aspects: task allocation and task deadline assignment. In order to satisfy real-time restrictions we combine genetic search and simulation to fine tune the system’s configuration. To reduce the solution search space, we propose a hybrid technique for finding feasible scheduling solutions. We determine task deadlines through a heuristic and then use the optimization-based approach to find a solution for task allocation to processors. We evaluate the performance of the proposed techniques by using automatically generated transaction sets. Finally, we compare the optimization-based technique with related work and we analyze the results.

Paper Nr: 35
Title:

Digital Database for Screening Mammography Classification Using Improved Artificial Immune System Approaches

Authors:

Rima Daoudi, Khalifa Djemal and Abdelkader Benyettou

Abstract: Breast cancer ranks first in the causes of cancer deaths among women around the world. Early detection and diagnosis is the key for breast cancer control, and it can increase the success of treatment, save lives and reduce cost. Mammography is one of the most frequently used diagnosis tools to detect and classify abnormalities of the breast. In this aim, Digital Database for Screening Mammography (DDSM) is an invaluable resource for digital mammography research, the purpose of this resource is to provide a large set of mammograms in a digital format. DDSM has been widely used by researchers to evaluate different computer-aided algorithms such as neural networks or SVM. The Artificial Immune Systems (AIS) are adaptive systems inspired by the biological immune system, they are able of learning, memorize and perform pattern recognition. We propose in this paper several enhancements of CLONALG algorithm, one of the most popular algorithms in the AIS field, which are applied on DDSM for breast cancer classification using adapted descriptors. The obtained classification results are 98.31% for CCS-AIS and 97.74% for MF-AIS against 95.57% for original CLONALG. This proves the effectiveness of the used descriptors in the two improved techniques.

Paper Nr: 36
Title:

A Boltzmann Multivariate Estimation of Distribution Algorithm for Continuous Optimization

Authors:

Ignacio Segovia-Domínguez, S. Ivvan Valdez and Arturo Hernández-Aguirre

Abstract: This paper introduces an approach for continuous optimization using an Estimation of Distribution Algorithm (EDA), based on the Boltzmann distribution. When using the objective function as energy function, the Boltzmann function favors the most promising regions, making the probability exponentially proportional to the objective function. Using the Boltzmann distribution directly for sampling is not possible because it requires the computation of the objective function values in the complete search space. This work presents an approximation to the Boltzmann function by a multivariate Normal distribution. Formulae for computing the mean and covariance matrix are derived by minimizing the Kullback-Leibler divergence. The proposed EDA is competitive and often superior to similar algorithms as it is shown by statistical results reported here.

Paper Nr: 41
Title:

Asynchronous Parallel (1+1)-CMA-ES for Constrained Global Optimisation

Authors:

Thomas Philip Runarsson

Abstract: The global search performance of an asynchrounous parallel (1+1) evolution strategy using the full covariance matrix adaptation for constrained optimization is presented. Although the (1+1)-CMA-ES may be a poor global optimizer it will be shown that within this parallel framework the global search performance can be enhanced significantly. This is achieved even when all individual (1+1) strategies use the same initial search point. The focus will be on constrained global optimization using a recently developed (1+1) evolution strategy for this purpose.

Paper Nr: 49
Title:

Combining Piecewise Linear Regression and a Granular Computing Framework for Financial Time Series Classification

Authors:

Valerio Modugno, Francesca Possemato and Antonello Rizzi

Abstract: Finance is a very broad field where the uncertainty plays a central role and every financial operator have to deal with it. In this paper we propose a new method for a trend prediction on financial time series combining a Linear Piecewise Regression with a granular computing framework. A set of parameters control the behavior of the whole system, thus making their fine tuning a critical optimization task. To this aim in this paper we employ an evolutionary optimization algorithm to tackle this crucial phase. We tested our system on both synthetic benchmarking data and on real financial time series. Our tests show very good classification results on benchmarking data. Results on real data, although not completely satisfactory, are encouraging, suggesting further developments.

Paper Nr: 53
Title:

Heuristic Crossover Operator for Evolutionary Induced Decision Trees

Authors:

Sašo Karakatič and Vili Podgorelec

Abstract: In this paper we propose an innovative and improved variation of genetic operator crossover for the classification decision tree models. Our improved crossover operator uses heuristic to choose the tree node that is exchanged to construct the children solutions. The algorithm selects a single node based on the classification accuracy and the usage of that particular node. We evaluate this method by comparing it with the results of the standard crossover method where nodes for exchange are chosen at random.

Paper Nr: 54
Title:

A Sharp Fitness Function for the Problem of Finding Roots of Polynomial Equations Systems

Authors:

Cruz E. Borges, José L. Montaña and Luis M. Pardo

Abstract: We experiment with several evolutionary algorithms for solving systems of polynomial equations with real coefficients. As a main difference with previous work, our algorithms can certify the correctness of the solutions they provide. This achievement is made possible by incorporating results from the field of numerical analysis to their fitness functions. We have performed an experimental comparison between the various proposed algorithms. The results of this comparison show that evolutionary and other local search algorithms can deal with the problem of solving systems of polynomial equations even for systems having many variables and high degree. Our main contribution is a nontrivial fitness function adjusted to the problem to be solved. This function is not based on any heuristics but on the fundamentals of numerical computation.

Paper Nr: 55
Title:

Study of Nuclear Reactor Reload Using Different Approaches of Quantum Inspired Algorithms

Authors:

Andressa dos Santos Nicolau and Roberto Schirru

Abstract: The purpose of this article is to show the performance of different approaches of quantum-inspired algorithms as optimization tool of Nuclear Reactor Reload of Brazilian Nuclear Power Plant. Nuclear Reactor Reload is a classical problem in Nuclear Engineering that has been studied for more than 40 years that focus on the economics and safety of the Nuclear Power Plant. The main goal of this article is to show the performance of Quantum Delta-Potential Well Based Particle Swarm Optimization Algorithm to solve the Nuclear Reactor Reload compared with its classical counterpart Particle Swarm Optimization with Random Keys method. Furthermore, others quantum inspired algorithms are also used to demonstrate the feasibility of quantum inspired algorithms to solve cycle 7 of Brazilian Nuclear Power Plant Angra 1. The results show that Quantum Delta-Potential Well Based Particle Swarm Optimization Algorithm found the best result with less computational effort than its classical counterpart. Besides shows that quantum inspired algorithm are well situated among the best alternatives for dealing with optimization problems that number of evaluations is crucial due to the high computational cost of the evaluations, such as Nuclear Reactor Reload.

Paper Nr: 57
Title:

Coherence Net - A New Model of Generative Cognition

Authors:

Michael O. Vertolli and Jim Davies

Abstract: We propose a new algorithm and formal description of generative cognition in terms of the multi-label bag-of-words paradigm. The algorithm, Coherence Net, takes its inspiration from evolutionary strategies, genetic programming, and neural networks. We approach generative cognition in spatial reasoning as the decompression of images that were compressed into lossy feature sets, namely, conditional probabilities of labels. We show that the globally parallel and locally serial optimization technique described by Coherence Net is better at accurately generating contextually coherent subsections of the original compressed images than a competitive, purely serial model from the literature: Coherencer.

Paper Nr: 60
Title:

Modelling of the Natural Evolutionary Computation in the Human Society

Authors:

Igor Weisband

Abstract: Under consideration is the theoretical model of social evolution proposed in 1851 by Herbert Spencer with a slightly modified interpretation of basic evolutionary concepts: adaptation, heredity, variation and selection. Some of the new concepts belong to informatics: the model (as the most important factor in adaptation and as the core of clans and elites gathering), marshaling and unmarshaling (heredity). Others were taken from sociology: ideologies, the revolutionary process (variation and selection). The paper presents an approach to computer simulation of these processes. It may seem that in the article too much sociology, but it is necessary in order to make the model most adequate. In addition, it is easier to explain the sociological aspects to computer scientists than aspects of informatics - to sociologists.

Paper Nr: 63
Title:

A Variable Neighborhood Search for Solving Sudoku Puzzles

Authors:

Khorshid Adel Hamza and Aise Zulal Sevkli

Abstract: Sudoku is a well-known puzzle that has achieved international popularity in the latest decade. Recently, there are explosive growths in the application of metaheuristic algorithms for solving Sudoku puzzles. In this paper, an algorithm based on Variable Neighborhood Search (VNS) is proposed to solve the Sudoku problem and the details of the implementation such as problem representation, neighbourhood structures are explained. The proposed algorithm is tested with Sudoku benchmarks which have been used in previous studies. The experimental results indicate that VNS is able to produce competitive results in easy level puzzles and promising results in medium and hard level puzzles.

Posters
Paper Nr: 8
Title:

Exploring the Impact of Different Classification Quality Functions in an ACO Algorithm for Learning Neural Network Structures

Authors:

Khalid M. Salama and Ashraf M. Abdelbar

Abstract: Although artificial neural networks can be a very effective classification method, one of the drawbacks of their use is the need to manually prescribe the neural network topology. Recent work has introduced the ANN-Miner algorithm, an Ant Colony Optimization (ACO) technique for optimizing the topology of arbitrary FFNN's, i.e. FFNN's with multiple hidden layers, layer-skipping connections, and without the requirement of full-connectivity between successive layers. In this paper, we explore the use of several classification quality evaluation functions in ANN-Miner. Our experimental results, using 30 popular benchmark datasets, identify several quality functions that significantly improve on the simple Accuracy quality function that was previously used in ANN-Miner.

Paper Nr: 9
Title:

A Possibility of Fast Running of Tyrannosaurus rex by the Result of Evolutionary Computation

Authors:

Yoshiyuki Usami

Abstract: The author examined the effectiveness of the optimization strategy of evolutionary computation and the conventional simulated annealing method when studying the locomotor motion of bipedal animals. The simulated annealing method is known as a powerful tool for finding near-optimal solutions for combinatorial problems such as the NP-complete problem. However, the author found the evolutionary computational strategy more effective at finding near-optimal solutions of the running motion of bipedal animals. The author conducted extensive simulations of the running motion of the large, bipedal dinosaur Tyrannosaurus rex based on realistic, biological parameters. The author’s simulations found that T.rex could run quickly, up to 14 m/s, which is faster than the beings.

Paper Nr: 10
Title:

Real Time Scheduling of Data Transmission Sessions in a Microsatellites Swarm and Ground Stations Network Based on Multi-Agent Technology

Authors:

P. Skobelev, E. Simonova, A. Ivanov, I. Mayorov, V. Travin and A. Zhilyaev

Abstract: The problem of designing an effective models and methods for data transmission between group of microsatellites and network of ground stations in the dynamically changing environment is considered. Multi-agent technology for solving the problem by adaptive resource allocation and scheduling is proposed. It is shown that solution of the considered complex problem evolutionary emerges from interaction and trade-offs of many agents which continuously self-organize themselves and change decisions to improve their objectives and the objectives of the system as a whole. The advantages of multi-agent solution are high adaptability, flexibility and efficiency of services. The main classes of agents, ontology of problem domain, interaction protocols, results of first experiments with system prototype and key benefits of proposed system are discussed.

Paper Nr: 11
Title:

Evolutionary Inheritance in Workflow Scheduling Algorithms within Dynamically Changing Heterogeneous Environments

Authors:

Nikolay Butakov, Denis Nasonov and Alexander Boukhanovsky

Abstract: State-of-the-art distributed computational environments requires increasingly flexible and efficient workflow scheduling procedures in order to satisfy the increasing requirements of the scientific community. In this paper, we present a novel, nature-inspired scheduling approach based on the leveraging of inherited populations in order to increase the quality of generated planning solutions for the occurrence of system events such as a computational resources crash or a task delay with the rescheduling phase .The proposed approach is based on a hybrid algorithm which was described in our previous work and includes strong points of list-based heuristics and evolutionary meta-heuristics principles. In this paper we also experimentally show that the proposed extension of hybrid algorithms generates more effective solutions than the basic one in dynamically heterogeneous computational changing environments.

Paper Nr: 20
Title:

A Differential Beta Quantum-behaved Particle Swarm Optimization for Circular Antenna Array Design

Authors:

Leandro dos Santos Coelho, Emerson Hochsteiner de Vasconcelos Segundo, Fabio Alessandro Guerra and Viviana Cocco Mariani

Abstract: The classical particle swarm optimization (PSO) algorithm is inspired on biological behaviors such as the social behavior of bird flocking and fish schooling. In this context, many significant improvements related the updating formulas and new operators have been proposed to improve the performance of the PSO algorithm in the literature. On the other hand, recently, as an alternative to the classical PSO, a quantum-behaved particle swarm optimization (QPSO) algorithm was proposed. The contribution of this paper is linked with a modified QPSO based on beta probability distribution and mutation differential operator. The effectiveness of the proposed modified QPSO algorithm is demonstrated by solving three kinds of optimization problems including two benchmark functions and a circular antenna design problem.

Paper Nr: 25
Title:

DMQEA: Dual Multiobjective Quantum-inspired Evolutionary Algorithm

Authors:

Si-Jung Ryu, Jong-Hwan Kim and Ki-Baek Lee

Abstract: This paper proposes dual multiobjective quantum-inspired evolutionary algorithm (DMQEA) with the dualstage of dominance check by introducing secondary objectives in addition to primary objectives. The secondary objectives are to maximize global evaluation values and crowding distances of the solutions in the external global population obtained for the primary objectives and the previous archive obtained from the secondary objectives-based nondominated sorting. By employing the secondary objectives for sorting the solutions in each generation, DMQEA can induce the balanced exploration of the solutions in terms of user’s preference and diversity to generate preferable and diverse nondominated solutions in the archive. To demonstrate the effectiveness of the proposed DMQEA, empirical comparisons with MQEA, MQEA-PS, and NSGA-II are carried out for benchmark functions.

Paper Nr: 29
Title:

Enhanced Flower Pollination Approach Applied to Electromagnetic Optimization

Authors:

Carlos Eduardo Klein, Emerson Hochsteiner de Vasconcelos Segundo, Viviana Cocco Mariani and Leandro dos Santos Coelho

Abstract: It is difficult to use the deterministic mathematical tools such as a gradient method to solve global optimization problems. Flower pollination algorithm (FPA) is a new nature-inspired algorithm of the swarm intelligence field to global optimization applications, based on the characteristics of flowering plants. To enhance the performance of the standard FPA, an enhanced FPA (EFPA) approach based on beta probability distribution was proposed in this paper. In order to verify the performance of the proposed EFPA, five benchmark functions are chosen from the literature as the test suit. Furthermore, tests using Loney’s solenoid benchmark, a classical problem in the electromagnetics area, are realized to evaluate the effectiveness of the FPA and the proposed EFPA. Simulation results and comparisons with the FPA demonstrated that the performance of the EFPA approach is promising in electromagnetics optimization.

Paper Nr: 38
Title:

Implementing a Software Cache for Genetic Programming Algorithms for Reducing Execution Time

Authors:

Savvas Karatsiolis and Christos Schizas

Abstract: A cache holding reusable computations that are carried out during the execution of a genetic algorithm is implemented and maintained in order to improve the performance of the genetic algorithm itself. The main idea is that the operational genome is actually consisting of small computational blocks that tend to be interchanged and reused several times before they complete (or not) their lifecycle. By computing these blocks once and keeping them in memory for future possible reuse, the algorithm is allowed to run up to fifty times faster according experimental results maintaining a general case execution time reduction of four times. The consistency of the cache is maintained through simple rules that validate entries in a very straight forward manner during the genetic operations of cross over and mutation.

Paper Nr: 48
Title:

An Ordering Procedure for Admissible Network Configurations to Regularize DFR Optimization Problems in Smart Grids

Authors:

A. Rizzi, F. Possemato, S. Caschera, M. Paschero and F. M. Frattale Mascioli

Abstract: The power loss reduction is one of the main targets for any electrical energy distribution company. In this paper the problem of the joint optimization of both topology and network parameters in a real Smart Grid is faced. A portion of the Italian electric distribution network managed by the ACEA Distribuzione S.p.A. located in Rome is considered. It includes about 1200 user loads, 70 km of Medium Voltage (MV) lines, 6 feeders, a Thyristor Voltage Regulator (TVR) and 6 distributed energy sources (5 generator sets and 1 photovoltaic plant). The power factor correction (PFC) is performed tuning the 5 generator sets and setting the state of the breakers in order to perform the distributed feeder reconfiguration (DFR). The joint PFC and DFR problem is faced by considering a suited objective function and by adopting a genetic algorithm. In this paper we present a heuristic method to compare the graphs of two admissible topologies, such that similar graphs are characterized by close active power loss values. This criterion is used to define a suited ordering of the list of admissible configurations, aiming to improve the continuity of the fitness function to the variation of the configurations parameter. Tests are performed by feeding the simulation environment with real data concerning dissipated and generated active and reactive power values. Preliminary results are very interesting, showing that, for the considered real network, the proposed ordering criteria for admissible network configurations can facilitate the optimization process.

Paper Nr: 61
Title:

An Open Architecture for Affective Traits in a BDI Agent

Authors:

Bexy Alfonso, Emilio Vivancos and Vicent J. Botti

Abstract: Recently an increasing amount of research focuses on improving agents believability by adding affective features to the traditional agent-based modeling. This is probably due to the demand of reaching ever more realistic behaviors on agent-based simulations which extends to several and diverse application fields. The present work proposes O3A: an Open Affective Agent Architecture, which extends a traditional BDI agent architecture improving a practical reasoning with more "human" characteristics. This architecture tries to address disperse definitions combining the main elements of supporting psychological and neurological theories.

Paper Nr: 64
Title:

Self Recommendation in Peer to Peer Systems

Authors:

Agostino Forestiero

Abstract: Recommendation system aims to produce a set of significant and useful suggestions that can be meaningful for a particular user. This paper introduces a self-organizing algorithm that by exploiting of a decentralized strategy builds a distributed recommendation system. The available resources are represented by a string of bits namely describer. The describers are obtained by exploiting of a locality preserving hash function that maps similar resources into similar strings of bits. Each pear works independently with the aim to locate the similar describer in neighbor peers. The peer decisions are based on the application of ad-hoc probability functions. The outcome will be a fast recommendation service thanks to the emergent sorted overlay-network. Preliminaries experimental results show as the logical reorganization can improve the recommendation operations.

Paper Nr: 65
Title:

A Generalization of the CMA-ES Algorithm for Functions with Matrix Input

Authors:

Simon Konzett

Abstract: This paper proposes a novel modification to the covariance matrix adaptation evolution strategy (CMA-ES) introduced by (Hansen and Ostermeier, 1996) under a special problem setting. In this paper the case is considered when the function which has to be optimized takes a matrix as input. Here an approach is presented where without vectorizing directly matrices are sampled and column and row-wise covariance matrices are adapted in each iteration of the proposed evolution strategy to adapt the mutation distribution. The method seems to be able to capture correlations in the entries of the considered matrix and adapt the corresponding covariance matrices accordingly. Numerical tests are performed on the proposed method to show advantages and disadvantages.