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Poster Session Programme

, the European Conference on the Applications of Evolutionary Computation -formerly known as EvoWorkshops- brings together researchers in a variety of areas of application of Evolutionary Computation and other Nature-inspired techniques.


Accepted papers will appear in the proceedings of EvoStar, published in a volume of the Springer Lecture Notes in Computer Science, which will be available at the Conference.Submissions must be original and not published elsewhere. The submissions will be peer reviewed by at least three members of the program committee. The authors of accepted papers will have to improve their paper on the basis of the reviewers comments and will be asked to send a camera ready version of their manuscripts. At least one author of each accepted work has to register for the conference and attend the conference and present the work.The reviewing process will be double-blind, please omit information about the authors in the submitted paper.

Submission Details

Submissions must be original and not published elsewhere. They will be peer reviewed by members of the program committee. The reviewing process will be double-blind, so please omit information about the authors in the submitted paper.

Submit your manuscript in Springer LNCS format.

Please provide up to five keywords in your Abstract

Page limit: 12 pages to http://myreview.csregistry.org/evoapps14/.


Submission deadline: 1 November 2013 11 November 2013
Notification: 06 January 2014
Camera ready: 01 February 2014
EvoApplications: 23-25 April 2014


Further information on the conference and co-located events can be
found in: http://www.evostar.org

EvoAPPS Posters

Wed 1745-1900  EvoAPPS posters

A hybrid swarm algorithm for post enrollment based course timetabling problem     Cheng Weng Fong, Hishammuddin Asmuni, Way Shen Lam, Barry McCollum, Paul McMullan 
The hybridization of metaheuristic approaches in solving optimization problems is increasingly the subject of many research projects. An Important objective of hybridizing two or more metaheuristic approaches is to attain a balance between global exploration and local exploitation within the overall search process. This paper presents such an approach, hybridising the population based approach Artificial Bee Colony (ABC) algorithm with a Hill Climbing (HC) local search and applying to the post enrollment-based course timetabling problem. In addition, the global best concept inspired from Particle Swarm Optimization (PSO) is used to improve the exploration ability of the basic ABC algorithm. The proposed hybrid approach is tested on Socha’s course timetabling datasets and is compared against state-of-the-art approaches from the literature. Experimental results demonstrate that the proposed hybrid approach outperforms the basic artificial bee colony algorithm and is comparable to other approaches published in the literature.

Adaptive Genetic Algorithm to Select Training Data for Support Vector Machines     Jakub Nalepa, Michal Kawulok
This paper presents a new adaptive genetic algorithm (AGA) to select training data for support vector machines (SVMs). SVM training data selection strongly influences the classification accuracy and time, especially in the case of large and noisy data sets. In the proposed AGA, a population of solutions evolves with time. The AGA parameters, including the chromosome length, are adapted according to the current state of exploring the solution space. We propose a new multi-parent crossover operator for an efficient search. A new metric of distance between individuals is introduced and applied in the AGA. It is based on the fast analysis of the vectors distribution in the feature space obtained using principal component analysis. An extensive experimental study performed on the well-known benchmark sets along with the real-world and artificial data sets, confirms that the AGA outperforms a standard GA in terms of the convergence capabilities. Also, it reduces the number of support vectors and allows for faster SVM classification.

A Multi-objective Evolutionary Approach for Cloud Service Provider Selection Problems with Dynamic Demands     Hsin-Kai Chen, Cheng-Yuan Lin, Jianhung Chen
This paper describes a multi-objective evolutionary approach for solving cloud computing service provider selection problems with dynamic demands. In this investigated problem, not only the service purchase costs and transmission costs of service providers are different, but the demands of service requests also change over the given periods. The objective of this problem is to select a number of cloud service provider while optimizing the total service distance, the total number of serviced demand points, the total service purchase costs, and total transmission costs simultaneously in the given continuous time periods. A multi-objective genetic approach with a seeding mechanism is proposed to solve the investigated problems. Four trail benchmark problems are designed and solved using the proposed multi-objective evolutionary algorithm. The results indicate that the proposed approach is capable of obtaining a number of non-dominated solutions for decision makers.

An Effective Nurse Scheduling by a Parameter Free Cooperative GA     Makoto Ohki
This paper describes a technique of penalty weight adjustment for the Cooperative Genetic Algorithm applied to the nurse scheduling problem. In this algorithm, coefficients and thresholds for each penalty function are automatically optimized. Therefore, this technique provides a parameter free algorithm of nurse scheduling. The nurse scheduling is very complex task, because many requirements must be considered. These requirements are implemented by a set of penalty function in this research. In real hospital, several changes of the schedule often happen. Such changes of the shift schedule yields various inconveniences, for example, imbalance of the number of the holidays and the number of the attendance. Such inconvenience causes the fall of the nursing level of the nurse organization. Reoptimization of the schedule including the changes is very hard task and requires very long computing time. We consider that this problem is caused by the solution space having many local minima. We propose a technique to adjust penalty weights and thresholds through the optimization to escape from the local minimal area.

An Improved Multiobjective Electromagnetism-like Mechanism Algorithm  Pedro Carrasqueira, Maria João Alves, Carlos Henggeler Antunes
Electromagnetism-like Mechanism (EM) is a population based optimization approach, which has been recently adapted to solve multiobjective (MO) problems (MOEM). In this work, an enhanced multiobjective Electromagnetism-like Mechanism algorithm is proposed (EMOEM). To assess this new algorithm, a comparison with MOEM algorithm is performed. Our aim is to assess the ability of both algorithms in a wide range of continuous optimization problems including benchmark problems with two and three objective functions. Experiments show that EMOEM performs better in terms of convergence and diversity when compared with the MOEM algorithm.

An object-oriented library in JavaScript to build modular and flexible cross-platform evolutionary algorithms     Victor Manuel Rivas Santos, Maria Isabel Garcia Arenas, Juan Julian Merelo Guervos, Antonio Mora Garcia, Gustavo Romero Lopez
This paper introduces jsEO, a new evolutionary computation library that is executed in web browsers, as it is written in Javascript. The library allows the rapid development of evolutionary algorithm, and makes easier the collaboration between different clients by means of individuals stored in a web server. In this work, jsEO has been tested against two simple problems, such as the Royal Road function and a 128-terms equation, and analysing how many machines and evaluations it yields. This paper attempts to reproduce results of older papers using modern browsers and all kind of devices that, nowadays, have JavaScript integrated in the browser, and is a complete rewrite of the code using the popular MooTools library. Results show that the system makes easier the development of evolutionary algorithms, suited for different chromosomes representations and problems, that can be simultaneously executed in many different operating systems and web browsers, sharing the best solutions previously found.

Automated Framework for General-Purpose Genetic Algorithms in FPGAs     Liucheng Guo, David Thomas, Wayne Luk
FPGA-based Genetic Algorithms (GAs) have been effective for optimisation of many real-world applications, but require extensive customisation of the hardware GA architecture. To promote these accelerated GAs to potential users without hardware design experience, this paper proposes an automated framework for creating and executing general-purpose GAs in FPGAs. The framework contains a scalable and customisable hardware architecture, which provides a unified platform for both binary and real-valued chromosomes. At compile-time, a user only needs to provide a high-level specification of the target application, without writing any hardware-specific code in low-level languages such as VHDL or Verilog. At run-time, a user can tune application inputs and GA parameters without time-consuming recompilation, in order to find a good configuration for further GA executions. The framework is demonstrated on a high performance FPGA platform to solve six problems and benchmarks, including a locating problem and the NP-hard set covering problem. Experiments show our custom GA is more flexible and easier to use compared to existing FPGA-based GAs, and achieves an average speed-up of 30 times compared to a multi-core CPU.

Automatic Selection of GA Parameters for Fragile Watermarking     Marco Botta, Davide Cavagnino, Victor Pomponiu
Genetic Algorithms (GAs) are known to be valuable tools for optimization purposes. In general, GAs can find good solutions by set ting their configuration parameters, such as mutation and crossover rates, population size, etc., to standard (i.e., widely used) values. In some application domains, changing the values of these parameters does not improve the quality of the solution, but m ight influence the ability of the algorithm to find such solution. In other application domains, fine tuning these parameters co uld result into a significant improvement of the solution quality. In this paper we present an experimental study aimed at findin g how fine tuning the parameters of a GA used for the insertion of a fragile watermark into a bitmap image influences the quality of the resulting digital object. However, when proposing a GA based new tool to non-expert users, selecting the best parameter s etting is not an easy task. Therefore, we will suggest how to automatically set the GA parameters in order to meet the quality an d/or running time performances requested by the user.

Classification of Potential Multiple Sclerosis Lesions through Automatic Knowledge Extraction by means of Differential Evolution     Ivanoe De Falco
In this paper a classifi er, designed by taking the user-friendliness issue into account, is described and is used to tackle the problem of classification of potential lesions in Multiple Sclerosis. This tool is based on the idea of making use of Di_fferential Evolution (DE) to extract explicit knowledge from a database under the form of a set of IF-THEN rules, can use this set of rules to carry out the classi fication task, and can also provide clinicians with this knowledge, thus explaining the motivation for each of the proposed diagnoses. Each DE individual codes for a set of rules. The tool is compared over a database of Multiple Sclerosis potential lesions against a set of nine classi fication tools widely used in literature. Furthermore, the usefulness and the meaningfulness of the extracted knowledge have been assessed by comparing it against that provided by Multiple Sclerosis experts. No great differences have turned out to exist between these two forms of knowledge.

Co-Evolutionary Optimization of Autonomous Agents in a Real-Time Strategy Game    Antonio J. Fernández Ares, Antonio M. Mora, Maribel García Arenas, Pablo García Sánchez, Juan Julian Merelo Guervós, Pedro A. Castillo
This paper presents an approach based in an evolutionary algorithm, aimed to improve the behavioral parameters which guide the actions of an autonomous agent (bot) inside the real-time strategy game, Planet Wars. The work describes a co-evolutionary implementation of a previously presented method (ANONYMOUSBot), which yielded successful results, but focused in 4 vs 4 matches this time. Thus, there have been analyzed the effects of considering several individuals to be evolved (improved) at the same time in the algorithm, along with the use of three different fitness functions measuring the goodness of each bot in the evaluation. They are based in turns and position, and also in mathematical computations of linear regression and area regarding the number of ships belonging to the bot/individual to be evaluated. In addition, the variance of using an evolutionary algorithm with and without previous knowledge in the co-evolution phase is also studied, i.e., respectively using specific rivals to perform the evaluation, or just considering to this end individuals in the population being evolved. The aim of these co-evolutionary approaches are mainly two: first, reduce the computational time; and second find a robust fitness function to be used in the generation of evolutionary bots optimized for 4 vs 4 battles.

Impact of the Topology on the Performance of Distributed Differential Evolution    Ivanoe De Falco, Antonio Della Cioppa, Domenico Maisto, Umberto Scafuri, Ernesto Tarantino
Migration topology plays a key role in designing effective distributed evolutionary algorithms. In this work we investigate the impact of several network topologies on the performance of a stepping-stone structured Differential Evolution model. Although some issues on the control parameters of the migration process and the way they affect the efficiency of the algorithm and the solution quality deserve further evaluative study, the influence of the topology on the performance both in terms of solution quality and convergence rate emerges from the empirical findings carried out on a set of test problems.

Modeling the offloading of different types of mobile applications by using evolutionary algorithms     Gianluigi Folino, Francesco Pisani
Modern smartphones permit to run a large variety of applications, i.e. multimedia, games, social network applications, etc. However, that considerably reduces the battery life of these devices. A possible solution to alleviate this problem is to offload part of the application or the whole computation to remote servers, i.e. Cloud Computing. The offloading cannot be performed without considering the issues derived from the nature of the application (i.e. multimedia, games, etc.), which can considerably change the resources necessary to the computation and the type, the frequency and the amount of data to be exchanged with the network. This work shows a framework for automatically building models for the offloading of mobile applications based on evolutionary algorithms and how it can be used to simulate different kinds of mobile applications and to analyze the rules generated. To this aim, a tool for generating mobile datasets, presenting different features, is designed and experiments are performed in different usage conditions in order to demonstrate the utility of the overall framework.

Multi-material compositional pattern-producing networks for form optimisation     Ralph Evins, Ravi Vaidyanathan, Stuart Burgess
CPPN-NEAT (Compositional Pattern Producing Networks and NeuroEvolution for Augmented Topologies) is a representation and optimisation approach that can generate and optimise complex forms without any pre-defined structure by using indirect, implicit representations. CPPN is based on an analogy to embryonic development; NEAT is based on an analogy to neural evolution. We present new developments that extend the approach to include multi-material objects, where the material distribution must be optimised in parallel with the form. Results are given for a simple problem concerning PV panels to validate the method. This approach is applicable to a large number of problems concerning the design of complex forms. There are many such problems in the field of energy saving and generation, particularly those areas concerned with solar gain.

Multiobjective Approach to Optimize Cantilever Walls Cost in Civil Engineering     Jesus Torrecilla-Pinero, Fernando Torrecilla-Pinero, Juan A. Gomez-Pulido, Carlos Uruena-Fernandez
A cantilever wall is a structure very used in civil engineering to retain earth and save different ground levels. There is a huge collection of possible designs of cantilever walls, where several restrictions must be considered to guarantee some limit states established by governmental laws in order to ensure a proper stability of the structure. We can define these limit states as situations that, if reached, suppose some kind of failure in the structure: sliding, settlement, flexural breaking, etc. In addition, we must impose a certain design factor for each limit state, so that, if all bad effects would be multiplied by such factor, the wall would still be stable. The wall design is defined by geometrical variables that suppose an economic cost, and several methods select their values to obtain a feasible wall. Some of these attempts configure a monoobjetive optimization problem to minimize the economic cost. In this paper we tackle a novel approach configuring a multiobjective optimization problem where the different design factors are objectives to be maximized. We have observed the optimum design lies in a region of the design space where small increments of the cost drives to a much safer design.

Objective Dimension and Problem Structure in Multiobjective Optimization Problems    Ramprasad Joshi, Bharat Deshpande, Paritosh Gote
Multiobjective optimization seeks simultaneous minimization of multiple scalar functions on R^n. Unless weighted sums are made to replace the vector functions arising thus, such an optimization requires some partial- or quasi-ordering of points in the search space based on comparisons between the values attained by the functions to be optimized at those points. Many such orders can be defined, and search-based (mainly heuristic) optimization algorithms make use of such orders implicitly or explicitly for refining and accelerating search. In this work, such relations are studied by modeling them as graphs. Information apparent in the structure of such graphs is studied in the form of degree distribution. It is found that when the objective dimension grows, the degree distribution tends to follow a power-law. This can be a new beginning in the study of escalation of hardness of problems with dimension, as also a basis for designing new heuristics.

Searching for Risk in Large Complex Spaces     Kester Clegg, Rob Alexander
ASHiCS (Automating the Search for Hazards in Complex Systems) uses evolutionary search on air traffic control simulations to find scenario configurations that generate high risk for a given air sector. Weighted heuristics are able to focus on specific events, flight paths or aircraft so that the search can effectively target incidents of interest. We describe how work on the characterization of our solution space suggests that destructive mutation operators perform badly in sensitive, high dimensional spaces. Finally, our work raises some issues about using collective risk assessment to discover significant safety events and whether the results are useful to safety analysts.

Sharing Information in Adversarial Bandit     David L. St-Pierre, Olivier Teytaud
2-Player games in general provide a popular platform for research in Artificial Intelligence (AI). One of the main challenges coming from this platform is approximating a Nash Equilibrium (NE) over zero-sum matrix games. While the problem of computing such a Nash Equilibrium is solvable in polynomial time using Linear Programming (LP), it rapidly becomes infeasible to solve as the size of the matrix grows; a situation commonly encountered in games. This paper focuses on improving the approximation of a NE for matrix games such that it outperforms the state-of-the-art algorithms given a finite (and rather small) number $T$ of oracle requests to rewards. To reach this objective, we propose to share information between the different relevant pure strategies. We show both theoretically by improving the bound and empirically by experiments on artificial matrices and on a real-world game that information sharing leads to an improvement of the approximation of the NE.

The structure of a probabilistic 1-state transducer representation for Prisoner's Dilemma     Jeffrey Tsang
In the study of evolutionary game theory, a tool called the fingerprint was developed. This mathematical technique generates a functional summary of an arbitrary game-playing strategy independent of representational details. Using this tool, this study expands the boundaries of investigating an entire small state space of strategies, to wit the probabilistic 1-state tranducers, as a representation for playing iterated Prisoner's Dilemma. A sampled grid of 35,937 strategies out of the continuous cube was used: they are fingerprinted and pairwise distances computed. A subsampled grid of 4,913 strategies was analyzed using metric multidimensional scaling. The results show that the known 3-dimensional manifold can be embedded into around 4--5 Euclidean dimensions without self-intersection, and the curvature of the fingerprint metric with respect to standard distance is not too extreme; there is also similarity with analogous results on other state spaces.

Tree depth influence in Genetic Programming for generation of competitive agents for RTS games     Pablo García-Sánchez, Antonio Fernández-Ares, Antonio Miguel Mora, Pedro Ángel Castillo, Juan Julián Merelo, Jesús González
This work presents the results obtained from comparing different tree depths in a Genetic Programming Algorithm to create agents that play the Planet Wars game. Three different maximum levels of the tree have been used (3, 7 and Unlimited) and two bots available in the literature, based on human expertise, and optimized by a Genetic Algorithm have been used for training and comparison. Results show that in average, the bots obtained using our method equal or outperform the previous ones, being the maximum depth of the tree a relevant parameter for the algorithm.

Unreliable Heterogeneous Workers in a pool-based evolutionary algorithm     Mario Garcia-Valdez, Juan-J. Merelo, Francisco Fernández-de-Vega
In this paper the effect of node unavailability in algorithms using EvoSpace, a pool-based evolutionary algorithm, is assessed. EvoSpace is a framework for developing evolutionary algorithms (EAs) using heterogeneous and unreliable resources. It is based on Linda's tuple space coordination model. The core elements of EvoSpace are a central repository for the evolving population and remote clients, here called EvoWorkers, which pull random samples of the population to perform on them the basic evolutionary processes (selection, variation and survival), once the work is done, the modified sample is pushed back to the central population. To address the problem of unreliable EvoWorkers, EvoSpace uses a simple re-insertion algorithm using copies of samples stored in a global queue which also prevents the starvation of the population pool. Using a benchmark problem from the P-Peaks problem generator we have compared two approaches: (i) the re-insertion of previous individuals at the cost of keeping copies of each sample, and a common approach of other pool based EAs, (ii) inserting randomly generated individuals. We found that EvoSpace is fault tolerant to highly unreliable resources and also that the re-insertion algorithm is only needed when the population is near the point of starvation.

Using a stochastic method to calibrate a vehicle emissions model     Neil Urquhart, Emma Hart, Wafaa Saleh
This paper examines the use of a stochastic technique (Evolutionary Algorithms) to lean the how to calibrate a vehicle emissions model that can be used to predict $CO_2$ emissions within a micro simulation. The model relies on the accurate calibration of parameters which represent quantities, such as rolling resistance and the relation between fuel consumption and energy for a particular vehicle, that are not readily available to the model user. We investigate whether a stochastic technique (an evolutionary algorithm) can be used to learn appropriate parameter values to predict $CO_2$ emissions. The learning is based upon data sets collected from instrumented vehicles driven in two UK cities. The experiments conducted show that the model can be successfully calibrated for both vehicles, with the error between predicted and actual outputs, typically $<\% . The process is compared to non-linear regression analysis, results show that the proposed process produces a more accurate emissions prediction mechanism over each observation point within a drive cycle. The authors demonstrate that complex emissions models may be successfully calibrated to individual characteristics of vehicles, in order that they can be used in activities such as micro modelling or more general vehicle routing applications. It is believed that this is the first time that stochastic methods have been utilised to calibrate vehicle emissions models.

What You Choose to See is What You Get: An Experiment with Learnt Sensory Modulation in a Robotic Foraging Task     Tiago Rodrigues, Miguel Duarte, Sancho Oliveira, Anders Christensen
In evolutionary robotics, the mapping from raw sensory input to neural network input is typically decided by the experimenter or encoded in the genome. Either way, the mapping remains fixed throughout a robot’s lifetime. Inspired by biological sensory organs and the mammalian brain’s capacity for selective attention, we evaluate an alternative approach in which a robot has active, real-time control over the mapping from sensory input to neural network input. We augment the neural controllers with additional output neurons that control key sensory parameters and evolve solutions for a single-robot foraging task. The results show that the capacity to control the mapping from raw input to neural network input is exploited by evolution and leads to novel solutions with higher fitness compared to traditional approaches.