ECTA 2016 Abstracts

Full Papers Short Papers
Full Papers
Paper Nr: 9
Title:

A Quantum Field Evolution Strategy - An Adaptive Surrogate Approach
Authors:

Jörg Bremer and Sebastian Lehnhoff
Abstract: Evolution strategies have been successfully applied to optimization problems with rugged, multi-modal fitness landscapes, to non linear problems, and to derivative free optimization. Usually evolution is performed by exploiting the structure of the objective function. In this paper, we present an approach that harnesses the adapting quantum potential field determined by the spatial distribution of elitist solutions as guidance for the next generation. The potential field evolves to a smoother surface leveling local optima but keeping the global structure what in turn allows for a faster convergence of the solution set. We demonstrate the applicability and the competitiveness of our approach compared with particle swarm optimization and the well established evolution strategy CMA-ES.
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Paper Nr: 14
Title:

Linked Genes Migration in Island Models
Authors:

Marcin Komarnicki and Michal Przewozniczek
Abstract: Island Models (IMs) divide the whole population into many coevolving subpopulations, which periodically exchange fractions of their individuals. Some IMs, exchange probabilistic models built during the subpopulations evolution. The use of many coevolving subpopulations helps to preserve the population diversity, which makes it less likely to get stuck in the local optima. Another promising research direction in the Evolutionary Computation field is the Linkage Learning. The knowledge about gene dependencies can be used in many different ways that improve the overall method effectiveness. Therefore, this paper proposes the Gene Pattern Based Island Model (GePIM) that uses the multi-population nature of IMs to generate the linkage information. GePIM also introduces a new type of migration based on exchanging linked gene groups, instead of exchanging the whole individuals or probabilistic models.
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Paper Nr: 16
Title:

DynaGrow – Multi-Objective Optimization for Energy Cost-efficient Control of Supplemental Light in Greenhouses
Authors:

Jan Corfixen Sørensen, Katrine Heinsvig Kjaer, Carl-Otto Ottosen and Bo Nørregaard Jørgensen
Abstract: The Danish greenhouse horticulture industry utilized 0.8 % of the total national electricity consumption in 2009 and it is estimated that 75 % of this is used for supplemental lighting. The increase in energy prices is a challenge for growers, and need to be addressed by utilizing supplemental light at low prices without compromising the growth and quality of the crop. Optimization of such multiple conflicting objectives requires advanced strategies that are currently not supported in existing greenhouse climate control systems. To incorporate advanced optimization functionality into existing systems is costly as the software is not designed for such changes. The growers can not afford to buy new systems or new hardware to address the changing objectives. DynaGrow is build on top of existing climate computers to utilize existing infrastructure. The greenhouse climate control problem is characterized by nonlinearity, stochasticity, non-convexity, high dimension of decision variables and an uncertain dynamic environment. Together, these mathematical properties are handled by applying a Multi-Objective Evolutionary Algorithm (MOEA) for discovering and exploiting critical trade-offs when optimizing the greenhouse climate. To formulate advanced objectives DynaGrow integrates local climate data, electricity energy price forecasts and outdoor weather forecasts. In spring 2015 one greenhouse experiment was executed to evaluate the effects of DynaGrow. The experiment was run as four treatments in four identical greenhouse compartments. One treatment was controlled by a standard control system and the other three treatments were controlled by different DynaGrow configurations. A number of different plant species and batches were grown in the four treatments over a season. The results from DynaGrow treatment demonstrated that it was clearly possible to produce a number of different sales-ready plant species and at the same time optimize the utility of supplemental light at low electricity prices without compromising quality.
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Paper Nr: 18
Title:

Dealing With Groups of Actions in Multiagent Markov Decision Processes
Authors:

Guillaume Debras, Abdel-Illah Mouaddib, Laurent Jean Pierre and Simon Le Gloannec
Abstract: Multiagent Markov Decision Processes (MMDPs) provide a useful framework for multiagent decision making. Finding solutions to large-scale problems or with a large number of agents however, has been proven to be computationally hard. In this paper, we adapt H-(PO)MDPs to multi-agent settings by proposing a new approach using action groups to decompose an initial MMDP into a set of dependent Sub-MMDPs where each action group is assigned a corresponding Sub-MMDP. Sub-MMDPs are then solved using a parallel Bellman backup to derive local policies which are synchronized by propagating local results and updating the value functions locally and globally to take the dependencies into account. This decomposition allows, for example, specific aggregation for each sub-MMDP, which we adapt by using a novel value function update. Experimental evaluations have been developed and applied to real robotic platforms showing promising results and validating our techniques.
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Paper Nr: 21
Title:

Evolving Art using Aesthetic Analogies - Evolutionary Supervised Learning to Generate Art with Grammatical Evolution
Authors:

Aidan Breen and Colm O'Riordan
Abstract: In this paper we describe an evolutionary approach using models of human aesthetic experience to evolve expressions capable of generating real-time aesthetic analogies between two different artistic domains. We outline a conceptual structure used to define aesthetic analogies and guide the collection of empirical data used to build aesthetic models. We also present a Grammatical Evolution based system making use of aesthetic models with a heuristic based fitness calculation approach to evaluate evolved expressions. We demonstrate a working model that has been designed to implement this system and use the evolved expressions to generate real-time aesthetic analogies with input music and output visuals. With this system we can generate novel artistic visual displays, similar to a light show at a music concert, which can react to the musician's performance in real-time.
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Paper Nr: 22
Title:

Particle Convergence Expected Time in The PSO Model with Inertia Weight
Authors:

Krzysztof Trojanowski and Tomasz Kulpa
Abstract: Theoretical properties of particle swarm optimization approach with inertia weight are investigated. Particularly, we focus on the convergence analysis of the expected value of the particle location and the variance of the location. Four new measures of the expected particle convergence time are defined: (1) convergence of the expected location of the particle, (2) the particle location variance convergence and (3-4) their respective weak versions. For the first measure an explicit formula of its upper bound is also given. For the weak versions of the measures graphs of recorded values are presented.
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Paper Nr: 29
Title:

Evolution of Cooperation in N-player Social Dilemmas: The Importance of being Mobile
Authors:

Maud D. Gibbons, Colm O'Riordan and Josephine Griffith
Abstract: This paper addresses issues regarding the emergence of cooperation in evolutionary, spatial game theoretic simulations. In the model considered, agents participate in a social dilemma with their neighbours and have the ability to move in response to environmental stimuli. Both the movement strategies and the game strategies (whether to cooperate or not) are evolved. In particular, we present results that compare the outcomes using the classical two player prisoner's dilemma and a generalised N-player prisoner's dilemma. We also explore the effect that agent density (the number of agents present per cell in the world) has on the evolution of cooperation in the environment. Finally, we discuss the movement strategies that are evolved for both cooperative and non-cooperative strategies.
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Paper Nr: 31
Title:

The Optional Prisoner's Dilemma in a Spatial Environment: Coevolving Game Strategy and Link Weights
Authors:

Marcos Cardinot, Colm O'Riordan and Josephine Griffith
Abstract: In this paper, the Optional Prisoner’s Dilemma game in a spatial environment, with coevolutionary rules for both the strategy and network links between agents, is studied. Using a Monte Carlo simulation approach, a number of experiments are performed to identify favourable configurations of the environment for the emergence of cooperation in adverse scenarios. Results show that abstainers play a key role in the protection of cooperators against exploitation from defectors. Scenarios of cyclic competition and of full dominance of cooperation are also observed. This work provides insights towards gaining an in-depth understanding of the emergence of cooperative behaviour in real-world systems.
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Paper Nr: 38
Title:

Evolution of Generic Square Calculations in Cellular Automata
Authors:

Michal Bidlo
Abstract: The paper deals with the design of uniform multi-state one-dimensional cellular automata using an evolutionary algorithm and their application to solve the problem of generic square calculations. The key idea is based on the representation of the transition functions for the automata, which utilises the concept of conditionally matching rules. This technique allows us to design complex cellular automata for which the conventional representations have failed. A study is proposed with various settings of the experimental system, which concerns the way of evaluating the candidate solutions, the number of cell states and the number of conditional rules of the transition functions. It is shown that various generic solutions for the square calculation can be obtained in one-dimensional cellular automata using local interactions of cells only. The results presented demonstrates an ability of the evolution to discover innovative solutions both from the view of complexity of the cellular automaton and the number of steps needed to calculate the results in comparison with the known solution.
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Short Papers
Paper Nr: 6
Title:

Enhanced Genetic Algorithm for Mobile Robot Path Planning in Static and Dynamic Environment
Authors:

Hanan Alsouly and Hachemi Bennaceur
Abstract: Path planning is an important component for a mobile robot to be able to do its job in different types of environments. Furthermore, determining the safest and shortest path from the start location to a desired destination, intelligently and in quickly, is a major challenge, especially in a dynamic environment. Therefore, various optimisation methods are recommended to solve the problem, one of these being a genetic algorithm (GA). This paper investigates the capabilities of GA for solving the path planning problem for mobile robots in static and dynamic environments. First, it studies the different GA approaches. Then, it carefully designs a new GA with intelligent crossover to optimise the search process in static and dynamic environments. It also conducts a comprehensive statistical evaluation of the proposed GA approach in terms of solution quality and execution time, comparing it against the well-known A* algorithm and MGA in a static scenario, and against the Improved GA in a dynamic scenario. The simulation results show that the proposed GA is able to find an optimal or near optimal solution with fast execution time compared to the three other algorithms, especially in large problems.
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Paper Nr: 8
Title:

Diversifying Techniques & Neutrality in Genetic Algorithms
Authors:

Seamus Hill and Colm O'Riordan
Abstract: This paper examines the implicit maintenance of diversity within a population through the inclusion of a layered genotype-phenotype map (GP-map) in a Genetic Algorithm (GA), based on the principal of Neutral theory. The paper compares a simple GA (SGA), incorporating a variety of diversifying techniques, to the multi-layered GA (MGA) as proposed by the authors. The MGA creates a neutral representation by including a layered GP-map based on the biological concepts of Transcription and Translation. In standard GAs, each phenotype is represented by a distinct genotype. However by allowing a higher number of alleles to encode phenotypic information on the genotype, one can create a situation where a number of genotypes may represent the same phenotype. Through this process one can introduce the idea of redundancy or neutrality into the representation. This representation allows for adaptive mutation (hot spots) and silent mutation (cold spots). This combination enables the level of diversity to dynamically adjust during the search, and directs the search towards closely related neutral sets. Previous work has shown that introducing this type of representation can be beneficial; in this paper we show how this representation is useful at introducing and maintaining diversity. Here we compare the performance of the MGA against traditional diversifying techniques used in conjunction with a SGA over a fully deceptive changing landscape.
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Paper Nr: 12
Title:

Neighborhood Strategies for QPSO Algorithms to Solve Benchmark Electromagnetic Problems
Authors:

Anton Duca, Laurentiu Duca, Gabriela Ciuprina and Daniel Ioan
Abstract: Several neighborhood strategies for QPSO algorithms are proposed and analyzed in order to improve the performances of the original methods. The proposed strategies are applied to some of the most well known QPSO algorithms such as the QPSO with random mean, the QPSO with Gaussian attractor and of course the basic QPSO. To prevent the premature convergence and to avoid being trapped in local minima the neighborhoods are dynamically changed during the optimization process. For testing the efficiency of the neighborhood techniques two benchmark optimization problems from the electromagnetic field computation have been chosen, Loney’s solenoid and TEAM22.
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Paper Nr: 13
Title:

GAdaBoost: Accelerating Adaboost Feature Selection with Genetic Algorithms
Authors:

Mai F. Tolba and Mohamed Moustafa
Abstract: Boosted cascade of simple features, by Viola Jones, is one of the most famous object detection frameworks. However, it suffers from a lengthy training process. This is due to the vast features space and the exhaustive search nature of Adaboost. In this paper we propose GAdaboost: a Genetic Algorithm to accelerate the training procedure through natural feature selection. Specifically, we propose to limit Adaboost search within a subset of the huge feature space, while evolving this subset following a Genetic Algorithm. Experiments demonstrate that our proposed GAdaboost is up to 3.7 times faster than Adaboost. We also demonstrate that the price of this speedup is a mere decrease (3%, 4%) in detection accuracy when tested on FDDB benchmark face detection set, and Caltech Web Faces respectively.
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Paper Nr: 23
Title:

A Meta-heuristic for Improving the Performance of an Evolutionary Optimization Algorithm Applied to the Dynamic System Identification Problem
Authors:

Ivan Ryzhikov, Eugene Semenkin and Evgenii Sopov
Abstract: In this paper a meta-heuristic for improving the performance of an evolutionary optimization algorithm is proposed. An evolutionary optimization algorithm is applied to the process of solving an inverse mathematical modelling problem for dynamical systems. The considered problem is related to the complex extremum seeking problem. The objective function and a method of determining a solution perform a class of optimization problems that require specific improvements of optimization algorithms. An investigation of algorithm efficiency revealed the importance of designing and implementing an operator that prevents population stagnation. The proposed meta-heuristic estimates the risk of the algorithm being stacked in a local optimum neighbourhood and it estimates whether the algorithm is close to stagnation areas. The meta-heuristic controls the algorithm and restarts the search if necessary. The current study focuses on increasing the algorithm efficiency by tuning the meta-heuristic settings. The examination shows that implementing the proposed operator sufficiently improves the algorithm performance.
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Paper Nr: 28
Title:

Bridging the Reality Gap — A Dual Simulator Approach to the Evolution of Whole-Body Motion for the Nao Humanoid Robot
Authors:

Malachy Eaton
Abstract: We describe a novel approach to the evolution of whole-body behaviours in the Nao humanoid robot using a multi-simulator approach to the alleviation of the reality gap issue. The initial evolutionary process takes place in the V-REP simulator. Once a viable whole-body motion has been evolved, this evolved motion is subsequently transferred for testing onto another simulation platform – Webots. Only when the evolved kicking behaviour has been demonstrated to also be viable on the Webots platform is this behaviour then transferred onto the real Nao robot for testing. This eliminates the time-consuming process of transferring behaviours onto the real robot which have little chance of successfully crossing the reality gap, and also minimises the potential for damage to the real Nao robot and/or it’s environment. By using this novel approach of employing two different simulators, each with its own individual strengths and weaknesses, we reduce the likelihood that any individual behaviour will be able to exploit individual simulators’ weaknesses, as the other simulator should pick up on this weak point. Using this procedure we have successfully evolved ball kicking behaviour in simulation, which has transferred with reasonable fidelity onto to the real Nao humanoid.
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Paper Nr: 32
Title:

Non-optimal Semi-autonomous Agent Behavior Policy Recognition
Authors:

Mathieu Lelerre and Abdel-Illah Mouaddib
Abstract: The coordination between cooperative autonomous agents is mainly based on knowing or estimating the behavior policy of each others. Most approaches assume that agents estimate the policies of the others by considering the optimal ones. Unfortunately, this assumption is not valid when we face the coordination problem between semi-autonomous agents where an external entity can act to change the behavior of the agents in a non-optimal way. We face such problems when the external entity is an operator guiding or tele-operating a system where many factors can affect the behavior of the operator such as stress, hesitations, preferences, ... In such situations the recognition of the other agent policies become harder than usual since considering all situations of hesitations or stress is not feasible. In this paper, we propose an approach able to recognize and predict future actions and behavior of such agents when they can follow any policy including non-optimal ones and different hesitations and preferences cases by using online learning techniques. The main idea of our approach is based on estimating, initially, the policy by the optimal one then we update it according to the observed behavior to derive a new estimated policy. In this paper, we present three learning methods of updating policies, show their stability and efficiency and compare them with existing approaches.
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Paper Nr: 34
Title:

An Analysis of Geometric Semantic Crossover: A Computational Geometry Approach
Authors:

Mauro Castelli, Luca Manzoni, Ivo Gonçalves, Leonardo Vanneschi, Leonardo Trujillo and Sara Silva
Abstract: Geometric semantic operators have recently shown their ability to outperform standard genetic operators on different complex real world problems. Nonetheless, they are affected by drawbacks. In this paper, we focus on one of these drawbacks, i.e. the fact that geometric semantic crossover has often a poor impact on the evolution. Geometric semantic crossover creates an offspring whose semantics stands in the segment joining the parents (in the semantic space). So, it is intuitive that it is not able to find, nor reasonably approximate, a globally optimal solution, unless the semantics of the individuals in the population ``contains'' the target. In this paper, we introduce the concept of convex hull of a genetic programming population and we present a method to calculate the distance from the target point to the convex hull. Then, we give experimental evidence of the fact that, in four different real-life test cases, the target is always outside the convex hull. As a consequence, we show that geometric semantic crossover is not helpful in those cases, and it is not even able to approximate the population to the target. Finally, in the last part of the paper, we propose ideas for future work on how to improve geometric semantic crossover.
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Paper Nr: 35
Title:

Breast Cancer Classification by Artificial Immune Algorithm based Validity Interval Cells Selection
Authors:

Rima Daoudi and Khalifa Djemal
Abstract: We present in this work an Artificial Immune System (AIS) algorithm for breast cancer classification and diagnosis. The main contribution is to select memory cells according to their belonging to a validity interval based on average similarity of training cells. The behaviour of these created memory cells preserves the diversity of original cancer learning class. All these operations allow to generate a set of memory cells with a global representativeness of the database which enables breast cancer classification and recognition. Promising results have been obtained on both Wisconsin Diagnosis Breast Cancer Database (WDBC) and (DDSM) Digital Database for Screening Mammography.
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Paper Nr: 37
Title:

Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks
Authors:

Stefano Beretta, Mauro Castelli, Ivo Gonçalves, Ivan Merelli and Daniele Ramazzotti
Abstract: Gene and protein networks are very important to model complex large-scale systems in molecular biology. Inferring or reverseengineering such networks can be defined as the process of identifying gene/protein interactions from experimental data through computational analysis. However, this task is typically complicated by the enormously large scale of the unknowns in a rather small sample size. Furthermore, when the goal is to study causal relationships within the network, tools capable of overcoming the limitations of correlation networks are required. In this work, we make use of Bayesian Graphical Models to attach this problem and, specifically, we perform a comparative study of different state-of-the-art heuristics, analyzing their performance in inferring the structure of the Bayesian Network from breast cancer data.
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Paper Nr: 42
Title:

Modelling Evolving Voting Behaviour on Internet Platforms - Stochastic Modelling Approaches for Dynamic Voting Systems
Authors:

Shikhar Raje, Navjyoti Singh and Shobhit Mohan
Abstract: Markov Decision Processes (MDPs) and their variants are standard models in various domains of Artificial Intelligence. However, each model captures a different aspect of real-world phenomena and results in different kinds of computational complexity. Also, MDPs are recently finding use in the scenarios involving aggregation of preferences (such as recommendation systems, e-commerce platforms, etc.). In this paper, we extend one such MDP variant to explore the effect of including observations made by stochastic agents, on the complexity of computing optimal outcomes for voting results. The resulting model captures phenomena of a greater complexity than current models, while being closer to a real world setting. The utility of the theoretical model is demonstrated by application to the real world setting of crowdsourcing. We address a key question in the crowdsourcing domain, namely, the Exploration Vs. Exploitation problem, and demonstrate the flexibility of adaptation of MDP-based models in Dynamic Voting scenarios.
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Paper Nr: 44
Title:

Reactive Collision Avoidance using Evolutionary Neural Networks
Authors:

Hesham M. Eraqi, Youssef Emad Eldin and Mohamed N. Moustafa
Abstract: Collision avoidance systems can play a vital role in reducing the number of accidents and saving human lives. In this paper, we introduce and validate a novel method for vehicles reactive collision avoidance using evolutionary neural networks (ENN). A single front-facing rangefinder sensor is the only input required by our method. The training process and the proposed method analysis and validation are carried out using simulation. Extensive experiments are conducted to analyse the proposed method and evaluate its performance. Firstly, we experiment the ability to learn collision avoidance in a static free track. Secondly, we analyse the effect of the rangefinder sensor resolution on the learning process. Thirdly, we experiment the ability of a vehicle to individually and simultaneously learn collision avoidance. Finally, we test the generality of the proposed method. We used a more realistic and powerful simulation environment (CarMaker), a camera as an alternative input sensor, and lane keeping as an extra feature to learn. The results are encouraging; the proposed method successfully allows vehicles to learn collision avoidance in different scenarios that are unseen during training. It also generalizes well if any of the input sensor, the simulator, or the task to be learned is changed.
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Paper Nr: 2
Title:

A Hybrid Multi-objective Immune Algorithm for Numerical Optimization
Authors:

Chris S. K. Leung and Henry Y. K. Lau
Abstract: With the complexity of real world problems, optimization of these problems often has multiple objectives to be considered simultaneously. Solving this kind of problems is very difficult because there is no unique solution, but rather a set of trade-off solutions. Moreover, evaluating all possible solutions requires tremendous computer resources that normally are not available. Therefore, an efficient optimization algorithm is developed in this paper to guide the search process to the promising areas of the solution space for obtaining the optimal solutions in reasonable time, which can aid the decision makers in arriving at an optimal solution/decision efficiently. In this paper, a hybrid multi-objective immune optimization algorithm based on the concepts of the biological evolution and the biological immune system including clonal selection and expansion, affinity maturation, metadynamics, immune suppression and crossover is developed. Numerical experiments are conducted to assess the performance of the proposed hybrid algorithm using several benchmark problems. Its performance is measured and compared with other well-known multi-objective optimization algorithms. The results show that for most cases the proposed hybrid algorithm outperforms the other benchmarking algorithms especially in terms of solution diversity.
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Paper Nr: 4
Title:

Multiobjective Adaptive Wind Driven Optimization
Authors:

Zikri Bayraktar and Muge Komurcu
Abstract: In this work, we introduce a new nature-inspired multiobjective numerical optimization algorithm where Pareto dominance is incorporated into Adaptive Wind Driven Optimization for handling multiobjective optimization problems and named as Multiobjective Adaptive Wind Driven Optimization (MO-AWDO) method. This new approach utilizes an external repository of air parcels to record the non-dominated Pareto-fronts found at each iteration via the fast non-dominated sorting algorithm, which are then utilized in the velocity update equation of the AWDO for the next iteration. The performance of the MO-AWDO is tested on five different numerical test functions with two objectives and results indicate that the MO-AWDO offers a very competitive approach compared to well-known methods in the published literature even performing better than NSGA-II for ZDT4 test function.
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Paper Nr: 7
Title:

EDA-based Decomposition Approach for Binary LSGO Problems
Authors:

Evgenii Sopov
Abstract: In recent years many real-world optimization problems have had to deal with growing dimensionality. Optimization problems with many hundreds or thousands of variables are called large-scale global optimization (LSGO) problems. Many well-known real-world LSGO problems are not separable and are complex for detailed analysis, thus they are viewed as the black-box optimization problems. The most advanced algorithms for LSGO are based on cooperative coevolution schemes using the problem decomposition. These algorithms are mainly proposed for the real-valued search space and cannot be applied for problems with discrete or mixed variables. In this paper a novel technique is proposed, that uses a binary genetic algorithm as the core technique. The estimation of distribution algorithm (EDA) is used for collecting statistical data based on the past search experience to provide the problem decomposition by fixing genes in chromosomes. Such an EDA-based decomposition technique has the benefits of the random grouping methods and the dynamic learning methods. The EDA-based decomposition GA using the island model is also discussed. The results of numerical experiments for benchmark problems from the CEC competition are presented and discussed. The experiments show that the approach demonstrates efficiency comparable to other advanced techniques.
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Paper Nr: 19
Title:

Ranking the Performance of Compiled and Interpreted Languages in Genetic Algorithms
Authors:

Juan Julián Merelo-Guervós, Israel Blancas-Álvarez, Pedro A. Castillo, Gustavo Romero, Pablo García-Sánchez, Víctor M. Rivas, Mario García-Valdez, Amaury Hernández-Águila and Mario Román
Abstract: Despite the existence and popularity of many new and classical computer languages, the evolu- tionary algorithm community has mostly exploited a few popular ones, avoiding them, especially if they are not compiled, under the asumption that compiled languages are always faster than interpreted languages. Wide-ranging performance analyses of implementation of evolutionary al- gorithms are usually focused on algorithmic implementation details and data structures, but these are usually limited to specific languages. In this paper we measure the execution speed of three common operations in genetic algorithms in many popular and emerging computer languages us- ing different data structures and implementation alternatives, with several objectives: create a ranking for these operations, compare relative speeds taking into account different chromosome sizes and data structures, and dispel or show evidence for several hypotheses that underlie most popular evolutionary algorithm libraries and applications. We find that there is indeed basis to consider compiled languages, such as Java, faster in a general sense, but there are other languages, including interpreted ones, that can hold its ground against them.
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Paper Nr: 20
Title:

EvoloPy: An Open-source Nature-inspired Optimization Framework in Python
Authors:

Hossam Faris, Ibrahim Aljarah, Seyedali Mirjalili, Pedro A. Castillo and Juan J. Merelo
Abstract: EvoloPy is an open source and cross-platform Python framework that implements a wide range of classical and recent nature-inspired metaheuristic algorithms. The goal of this framework is to facilitate the use of metaheuristic algorithms by non-specialists coming from different domains. With a simple interface and minimal dependencies, it is easier for researchers and practitioners to utilize EvoloPy for optimizing and benchmarking their own defined problems using the most powerful metaheuristic optimizers in the literature. This framework facilitates designing new algorithms or improving, hybridizing and analyzing the current ones. The source code of EvoloPy is publicly available at GitHub (https://github.com/7ossam81/EvoloPy).
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Paper Nr: 40
Title:

An Evolutionary Approach to Formation Control with Mobile Robots
Authors:

Jane Holland, Josephine Griffith and Colm O'Riordan
Abstract: The field of swarm robotics studies multi-robot systems, emphasising decentralised and self-organising behaviours that deal with limited individual abilities, local sensing and local communication. A robotic system needs to be flexible to environmental changes, robust to failure and scalable to large groups. These desired features can be achieved through collective behaviours such as aggregation, synchronisation, coordination and exploration. We aim to analyse these emerging behaviours by applying an evolutionary approach to a specific robotic system, called the Kilobot, in order to learn behaviours. If successful, not only would the cost and computation time for evolutionary computation in mobile robotics decrease, but the reality-gap could also narrow.
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Paper Nr: 41
Title:

Integrating Expectations into Jason for Appraisal in Emotion Modeling
Authors:

Joaquín Taverner, Bexy Alfonso, Emilio Vivancos and Vicente Botti
Abstract: Emotions have a strong influence on human reasoning and behavior, thus, in order to build intelligent agents which simulate human behavior, it is necessary to consider emotions. Expectations are one of the bases for emotion generation through the appraisal process. In this work we have extended the Jason agent language and platform for handling expectations. Unlike other approaches focused on expectations handling, we have modified the agent reasoning cycle to manage expectations, avoiding complex additional mechanisms such as monitors. This tool is part of the GenIA³ architecture and, hence, is a step towards the standardization of the emotion modeling process in BDI (Beliefs-Desires-Intentions) agents.
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Paper Nr: 43
Title:

DNA Analysis: Principles and Sequencing Algorithms
Authors:

Veronika Abramova, Bruno Cabral and Jorge Bernardino
Abstract: DNA discovery has put humans one step closer to deciphering their own structure stored as biological data. Such data could provide us with a huge amount of information, necessary for studying ourselves and learn all the variants that pre-determine one’s characteristics. Although, these days, we are able to extract DNA from our cells and transform it into sequences, there is still a long road ahead since DNA has not been easy to process or even extract in one go. Over the past years, bioinformatics has been evolving more and more, constantly aiding biologists on the attempts to “break” the code. In this paper, we present some of the most relevant algorithms and principles applied on the analysis of our DNA. We attempt to provide basic genome overview but, moreover, the focus of our study is on assembly, one of the main phases of DNA analysis.
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