ICEC 2009 Abstracts


Full Papers
Paper Nr: 17
Title:

CULTURAL SWARMS - Knowledge-driven Framework for Solving Nonlinearly Constrained Global Optimization Problems

Authors:

Mostafa Z. Ali, Yaser Khamayseh and Robert G. Reynolds

Abstract: In this paper we investigate how diverse knowledge sources interact to direct individuals in a swarm population influenced by a social fabric approach to efficiently solve nonlinearly constrained global minimization problems. We identify how knowledge sources used by Cultural Algorithms are combined to direct the decisions of the individual agents in solving optimization problems using an influence function family based upon a Social Fabric metaphor. The interaction of these knowledge sources with the population swarms produced emergent phases of problem solving. This reflected an algorithmic process that emerged from the interaction of the knowledge sources under the influence of a social fabric using different configurations. This suggests that the social interaction of individuals coupled with their interaction with a culture within which they are embedded provides a powerful vehicle for the solution of nonlinearly constrained optimization problems. The algorithm can escape from the previously converged local minimizers, and can converge to an approximate global minimizer of the problem asymptotically. Numerical experiments show that it is better than many other well-known recent methods for constrained global optimization.

Paper Nr: 38
Title:

EVOLUTIONARY DYNAMICS OF EXTREMAL OPTIMIZATION

Authors:

Stefan Böttcher

Abstract: Motivated by noise-driven cellular automata models of self-organized criticality (SOC), a new paradigm for the treatment of hard combinatorial optimization problems is proposed. An extremal selection process preferentially advances variables in a poor local state. The ensuing dynamic process creates broad fluctuations to explore energy landscapes widely, with frequent returns to near-optimal configurations. This Extremal Optimization heuristic is evaluated theoretically and numerically.

Paper Nr: 42
Title:

A STUDY OF GENETIC PROGRAMMING VARIABLE POPULATION SIZE FOR DYNAMIC OPTIMIZATION PROBLEMS

Authors:

Leonardo Vanneschi and Giuseppe Cuccu

Abstract: A new model of Genetic Programming with variable size population is presented in this paper and applied to the reconstruction of target functions in dynamic environments (i.e. problems where target functions change with time). The suitability of this model is tested on a set of benchmarks based on some well known symbolic regression problems. Experimental results confirm that our variable size population model finds solutions of similar quality to the ones found by standard Genetic Programming, but with a smaller amount of computational effort.

Paper Nr: 59
Title:

INTERACTIVE EVOLUTIONARY DESIGN OF MOTION VARIANTS

Authors:

Jonathan Eisenmann, Matthew Lewis and Bryan Cline

Abstract: This paper presents an intuitive method for novice users to interactively design custom populations of stylized, heterogeneous motion, from one input motion clip, thus allowing the user to amplify an existing database of motions. We allow the user to set up lattice deformers which are used by a genetic algorithm to manipulate the animation channels of the input motion and create new motion variations. Our interactive evolutionary design environment allows the user to traverse the available space of possible motions, presents the user with populations of motion, and gradually converges to a satisfactory set of solutions. Each generated motion sequence can undergo a motion filtering process subject to user-specified, high-level metrics to produce a result crafted to fit the designer’s interest.

Paper Nr: 61
Title:

THE DUAL PHASE EVOLUTION FRAMEWORK FOR UNDERSTANDING EVOLUTIONARY DYNAMICS IN COMPLEX ADAPTIVE SYSTEMS

Authors:

Greg Paperin and Suzanne Sadedin

Abstract: Evidence from several fields suggests that dual phase evolution (DPE) may account for distinctive features associated with complex adaptive systems. Here, we review empirical and theoretical evidence for DPE in natural systems and examine the relationship of DPE to self-organized criticality and adaptive cycles. A general model for DPE in networks is outlined, with preliminary data illustrating the emergence of phase changes.

Paper Nr: 66
Title:

A HYBRID ANT COLONY OPTIMIZATION ALGORITHM FOR SOLVING THE TERMINAL ASSIGNMENT PROBLEM

Authors:

Eugénia Moreira Bernardino, Anabela Moreira Bernardino, Juan Manuel Sánchez-Pérez, Juan Antonio Gomez Pulido and Miguel Ángel Vega-Rodríguez

Abstract: The past two decades have witnessed tremendous research activities in optimization methods for communication networks. One important problem in communication networks is the Terminal Assignment Problem. This problem involves determining minimum cost links to form a network by connecting a collection of terminals to a collection of concentrators. In this paper, we propose a Hybrid Ant Colony Optimization Algorithm to solve the Terminal Assignment Problem. We compare our results with the results obtained by the standard Genetic Algorithm, the Tabu Search Algorithm and the Hybrid Differential Evolution Algorithm, used in literature.

Paper Nr: 80
Title:

A COMPARATIVE STUDY OF NEIGHBORHOOD TOPOLOGIES FOR PARTICLE SWARM OPTIMIZERS

Authors:

Angelina Jane Reyes-Medina, Gregorio Toscano Pulido and José Gabriel Ramírez-Torres

Abstract: Particle swarm optimization (PSO) is a meta-heuristic that has been found to be very successful in a wide variety of optimization tasks. The behavior of any meta-heuristic for a given problem is directed by both: the variation operators, and the values selected for the parameters of the algorithm. Therefore, it is only natural to expect that not only the parameters, but also the neighborhood topology play a key role in the behavior of PSO. In this paper, we want to analyze whether the type of communication employed to interconnect the swarm accelerates or affects the algorithm convergence. In order to perform a wide study, we selected six different neighborhoods topologies: ring, fully connected, mesh, toroid, tree and star; and two clustering algorithms: k-means and hierarchical. Such approaches were incorporated into three PSO versions: the basic PSO, the Bare-bones PSO (BBPSO) and an extension of BBPSO called BBPSO(EXP). Our results indicate that the convergence rate of a PSO-based approach has an strongly dependence of the topology used. However, we also found that the topology most widely used is not necessarily the best topology for every PSO-based algorithm.

Paper Nr: 81
Title:

DYNAMIC AND EVOLUTIONARY MULTI-OBJECTIVE OPTIMIZATION FOR SENSOR SELECTION IN SENSOR NETWORKS FOR TARGET TRACKING

Authors:

Nikhil Padhye, Long Zuo, Chilukurii K. Mohan and Pramod K. Varshney

Abstract: When large sensor networks are applied to the task of target tracking, it is necessary to successively identify subsets of sensors that are most useful at each time instant. Such a task involves simultaneously maximizing target detection accuracy and minimizing querying cost, addressed in this paper by the application of multi-objective evolutionary algorithms (MOEAs). NSGA-II, a well-known MOEA, is demonstrated to be successful in obtaining diverse solutions (trade-off points), when compared to a ”weighted sum” approach that combines both objectives into a single cost function. We also explore an improvement, LS-DNSGA, which incorporates periodic local search into the algorithm, and outperforms standard NSGA-II on the sensor selection problem.

Short Papers
Paper Nr: 13
Title:

THE UPHILL BATTLE OF ANT PROGRAMMING VS. GENETIC PROGRAMMING

Authors:

Amirali Salehi-Abari and Tony White

Abstract: Ant programming has been proposed as an alternative to Genetic Programming (GP) for the automated production of computer programs. Generalized Ant Programming (GAP) – an automated programming technique derived from principles of swarm intelligence – has shown promise in solving symbolic regression and other hard problems. Enhanced Generalized Ant Programming (EGAP) has improved upon the performance of GAP; however, a comparison with GP has not been performed. This paper compares EGAP and GP on 3 well-known tasks: Quartic symbolic regression, multiplexer and an ant trail problem. When comparing EGAP and GP, GP is found to be statistically superior to EGAP. An analysis of the evolving program populations shows that EGAP suffers from premature diversity loss.

Paper Nr: 16
Title:

A MULTI-POPULATION GENETIC ALGORITHM FOR TREE-SHAPED NETWORK DESIGN PROBLEMS

Authors:

Dalila B. M. M. Fontes and José Fernando Gonçalves

Abstract: In this work we propose a multi-population genetic algorithm for tree-shaped network design problems using random keys. Recent literature on finding optimal spanning trees suggests the use of genetic algorithms. Furthermore, random keys encoding has been proved efficient at dealing with problems where the relative order of tasks is important. Here we propose to use random keys for encoding trees. The topology of these trees is restricted, since no path from the root vertex to any other vertex may have more than a pre-defined number of arcs. In addition, the problems under consideration also exhibit the characteristic of flows. Therefore, we want to find a minimum cost tree satisfying all demand vertices and the pre-defined number of arcs. The contributions of this paper are twofold: on one hand we address a new problem, which is an extension of the well known NP-hard hop-constrained MST problem since we also consider determining arc flows such that vertices requirements are met at minimum cost and the cost functions considered include a fixed cost component and a nonlinear flow routing component; on the other hand, we propose a new genetic algorithm to efficiently find solutions to this problem.

Paper Nr: 20
Title:

AN ADAPTIVE SWARM-BASED ALGORITHM FOR RESOURCE ALLOCATION IN DYNAMIC ENVIRONMENTS

Authors:

Tony White, Amirali Salehi-Abari and Gayan Abeysundara

Abstract: Dynamic allocation of agents to a set of tasks is a hard problem. Furthermore, having too few or too many agents can result in poor task completion owing to conflicting agent decisions thus creating the problem of the Tragedy of the Commons. This paper proposes a swarm-based algorithm inspired by the social behavior of ants that causes agent specialization to a particular task -- resource allocation in a spatial region -- and determines the near optimal number of agents required to complete the tasks presented. The utility of the algorithm is demonstrated by application to the dynamic allocation of frequencies in a cellular network.

Paper Nr: 21
Title:

SIMULATING ARTIST AND CRITIC DYNAMICS - An Agent-based Application of an Evolutionary Art System

Authors:

Gary Greenfield and Penousal Machado

Abstract: We describe an agent based artist-critic simulation. Artist agents use a swarm based evolutionary art system to evolve images that try to match their preferences. Preferred images are submitted to critic agents who then decide, accordingly to their own criteria, which images should be displayed in a public gallery. The purpose of our model is to enable the implementation of a variety of behavioral policies which result in different dynamics. A reward system determines the impact of each critic and the success of each artist, which in turn leads to behavioral and preference changes. The experimental results indicate the emergence of novel styles and trends, artist-critic cooperation, and niche exploitation.

Paper Nr: 22
Title:

EVOLUTIONARY PROGRAMMING GUIDED BY ANALYTICALLY GENERATED SEEDS

Authors:

Neil Crossley, Emanuel Kitzelmann, Martin Hofmann and Ute Schmid

Abstract: Evolutionary programming is the most powerful method for inducing recursive functional programs from input/output examples while taking into account efficiency and complexity constraints for the target program. However, synthesis time can be considerably high. A strategy which is complementary to the generate-and -test based approaches of evolutionary programming is inductive analytical programming where program construction is example-driven, that is, target programs are constructed as minimal generalization over the given input/output examples. Synthesis with analytical approaches is fast, but the scope of synthesizable programs is restricted. We propose to combine both approaches in such a way that the power of evolutionary programming is preserved and synthesis becomes more efficient. We use the analytical system IGOR2 to generate seeds in form of program skeletons to guide the evolutionary system ADATE when searching for target programs. In an evaluations with several examples we can show that using such seeds indeed can speed up evolutionary programming considerably.

Paper Nr: 32
Title:

SIMPLE GENETIC ALGORITHM WITH GENERALISED a*-SELECTION - Dynamical System Model, Fixed Points, and Schemata

Authors:

André Neubauer

Abstract: The dynamical system model proposed by VOSE provides a theory of genetic algorithms as specific random heuristic search (RHS) algorithms by describing the stochastic trajectory of a population with the help of a deterministic heuristic function and its fixed points. In order to simplify the mathematical analysis and to enable the explicit calculation of the fixed points the simple genetic algorithm (SGA) with a-selection has been introduced where the best or a-individual is mated with individuals randomly chosen from the population with uniform probability. This selection scheme also allows to derive a simple coarse-grained system model based on the equivalence relation imposed by schemata. In this paper, the a-selection scheme is generalised to a*-selection by allowing the ß best individuals of the current population instead of the single best a-individual to mate with other individuals randomly chosen from the population. It is shown that most of the results obtained for a-selection can be transferred to the SGA with generalised a*-selection, e.g. the explicit calculation of the fixed points of the heuristic function or the derivation of a coarse-grained system model based on schemata.

Paper Nr: 39
Title:

AN OPTIMAL SILVICULTURAL REGIME MODEL USING COMPETITIVE CO-EVOLUTIONARY GENETIC ALGORITHMS

Authors:

Oliver Chikumbo

Abstract: A competitive co-evolutionary genetic algorithm was successfully employed to determine an optimal silvicultural regime for the South African Pinus patula Schl. Et Cham. The solution to the silvicultural regime included: initial planting density; frequency, timing and intensity of thinnings; final crop number; and rotation length. The growth dynamics for P.patula were estimated using dynamical models, the building blocks of the combined optimal control and parameter selection formulation, with a single objective function that was maximised for value production. The results were compared against a silvicultural regime determined using Pontryagin’s Maximum Principle. Both the regimes were then compared against the recommended silvicultural regime determined from years of experimental trials. The genetic algorithms regime was superior to the other two.

Paper Nr: 49
Title:

MULTIOBJECTIVE EVOLUTIONARY OPTIMIZATION OF GREENHOUSE VEGETABLE CROP DISTRIBUTIONS

Authors:

A. L. Márquez, F. Manzano-Agugliaro, C. Gil, R. Cañero-León, F. G. Montoya and R. Baños

Abstract: Multiobjective evolutionary algorithms (MOEAs) are known for their ability to optimize several objective functions simultaneously to provide a representative set of the Pareto front, which is a set of problem solutions representing a trade-off between the best values of each one of the objectives. This characteristic is specially interesting for the optimization of many real world problems, such as the allocation of land resources to maximize profit while reducing the economical risks associated to different distributions of crops in southern Spain, which has one of the largest concentrations of greenhouses in the world.

Paper Nr: 54
Title:

THE FLY ALGORITHM REVISITED - Adaptation to CMOS Image Sensors

Authors:

Emmanuel Sapin, Jean Louchet and Evelyne Lutton

Abstract: Cooperative coevolution algorithms (CCEAs) usually represent a searched solution as an aggregation of several individuals (or even as a whole population). In other terms, each individual only bears a part of the searched solution. This scheme allows to use the artificial Darwinism principles in a more economic way, and the gain in terms of robustness and efficiency is important. In the computer vision domain, this scheme has been applied to stereovision, to produce an algorithm (the fly algorithm) with asynchronism property. However, this property has not yet been fully exploited, in particular at the sensor level, where CMOS technology opens perpectives to faster reactions. We describe in this paper a new coevolution engine that allow the Fly Algorithm to better exploit the properties of CMOS image sensors.

Paper Nr: 67
Title:

SOLVING THE NON-SPLIT WEIGHTED RING ARC-LOADING PROBLEM IN A RESILIENT PACKET RING USING PARTICLE SWARM OPTIMIZATION

Authors:

Anabela Moreira Bernardino, Eugénia Moreira Bernardino, Juan Manuel Sánchez-Pérez, Juan Antonio Gomez Pulido and Miguel Ángel Vega-Rodríguez

Abstract: Massive growth of the Internet traffic in last decades has motivated the design of high-speed optical networks. Resilient Packet Ring (RPR), also known as IEEE 802.17, is a standard designed for the optimized transport of data traffic over optical fiber ring networks. Its design is to provide the resilience found in SONET/SDH networks but instead of setting up circuit oriented connections, providing a packet based transmission. This is to increase the efficiency of Ethernet and IP services. In this paper, a weighted ring arc-loading problem (WRALP) is considered which arises in engineering and planning of the RPR systems (combinatorial optimization NP- complete problem). Specifically, for a given set of non-split and uni-directional point-to-point demands (weights), the objective is to find the routing for each demand (i.e., assignment of the demand to either clockwise or counter-clockwise ring) so that the maximum arc load is minimized. This paper suggests four variants of Particle Swarm Optimization (PSO), combined with a Local Search (LS) method to efficient non-split traffic loading on the RPR. Numerical simulation results show the effectiveness and efficiency of the proposed methods.

Paper Nr: 73
Title:

USING ATTAINMENT SURFACE FOR COMPARING NSGA-II AND SPEA-II - A Case Study

Authors:

Alvaro Gomes, C. Henggeler Antunes, A. Gomes Martins and João Melo

Abstract: This paper presents a comparative analysis of the results obtained with two different genetic algorithms, NSGA-II and SPEA-II, in the framework of load management activities in electric power systems. The multiobjective problem deals with the identification and the selection of suitable control strategies to be applied to groups of electric loads aimed at reducing maximum power demand at the sub-station level, maximizing profits with selling of electricity and minimizing the discomfort caused to the end-users. The comparative analysis of the algorithms’ performance is done based on the attainment surface approach. Besides, it is shown that this approach can be used as a vehicle to introduce the decision maker’s preferences in the evaluation process.

Paper Nr: 77
Title:

ARTIFICIAL LIFE MODEL OF DENGUE HOST-VECTOR DISEASE PROPAGATION

Authors:

Carlos Isidoro, Nuno Fachada, Fábio Barata and Agostinho Rosa

Abstract: The paper presents an agent based model of the Aedes aegypti mosquito, which considers mosquito population dynamics and a specific population control strategy, as well the dengue propagation in mosquito (vector) and human (host) populations. More specifically, this study concerns the impact that the RIDL strategy (Release of Insects carrying a Dominant Lethal gene) has on the infection period among humans. The agents model the main aspects of the mosquito’s ecology and behavior, while the environmental components are implemented as a layer of dynamic elements obeying to physical laws. The main objective of this approach is to provide realistic simulations of insect biologic control strategies, namely RIDL. Model verification was performed through examination of simulation parameters variation and qualitative assessment with existing models and simulations. The LAIS simulator was a valuable tool in this investigation, allowing efficient agent based modeling (ABM) and simulation deployment and analysis.

Paper Nr: 95
Title:

A TUNABLE REAL-WORLD MULTI-FUNNEL BENCHMARK PROBLEM FOR EVOLUTIONARY OPTIMIZATION - And Why Parallel Island Models Might Remedy the Failure of CMA-ES on It

Authors:

Christian L. Müeller and Ivo F. Sbalzarini

Abstract: A common shortcoming in the Evolutionary Computation (EC) community is that the publication of many search heuristics is not accompanied by rigorous benchmarks on a balanced set of test problems. A welcome effort to promote such test suites are the IEEE CEC competitions on real-valued black-box optimization. These competitions prescribe carefully designed synthetic test functions and benchmarking protocols. They do, however, not contain tunable real-world examples of the important class of multi-funnel functions. We argue that finding minimum-energy configurations of 38-atom Lennard-Jones (LJ38) clusters could serve as such a benchmark for real-valued, single-objective evolutionary optimization. We thus suggest that this problem be included in EC studies whenever general-purpose optimizers are proposed. The problem is tunable from a single-funnel to a double-funnel topology. We show that the winner of the CEC 2005 competition, the Evolution Strategy with Covariance Matrix Adaptation (CMA-ES), works on the single-funnel version of this test case, but fails on the double-funnel version. We further argue that this performance loss of CMA-ES can be relaxed by using parallel island models. We support this hypothesis by simulation results of a parallel island CMA-ES, the Particle Swarm CMA-ES, on a subset of the multi-funnel functions in the CEC 2005 benchmark.

Paper Nr: 97
Title:

LIVING SYSTEMS’ ORGANISATION AND PROCESSES FOR ACHIEVING ADAPTATION - Principles to Borrow from Biology

Authors:

Dragana Laketic and Gunnar Tufte

Abstract: Man–made systems, just like their biological counterparts, need to operate in a fluctuating environment. Living systems survive despite these fluctuations. Their viability is made possible due to the ability to adapt to environmental fluctuations. Such ability the living systems possess is due to the organisation of these systems and processes performed in response to fluctuation. Therefore, deeper understanding of those aspects of the living systems which make them adaptable may be beneficial for human designers when faced with the demands for the design of adaptive systems. This paper presents current state of our investigation and some interesting postulations about how adaptation process may be sustained until adaptation is achieved in the system under consideration. Further, we discuss some aspects of the living systems’ organisation which may offer useful guidelines for adaptive systems design.

Posters
Paper Nr: 9
Title:

MULTIOBJECTIVE TUNING OF ROBUST GPC CONTROLLERS USING EVOLUTIONARY ALGORITHMS

Authors:

J. M. Herrero, X. Blasco, M. Martínez and J. Sanchis

Abstract: In this article a procedure to tune robust Generalized Predictive Controllers (GPC) is presented. To tune the controller parameters a multiobjective optimization problem is formulated so the designer can consider conflicting objectives simultaneously without establishing any prior preference. Moreover model uncertainty, represented by a list of possible models, is considered. The multiobjective problem is solved with a specific Evolutionary Algorithm (ev-MOGA). Finally, an application to a non-linear thermal process is presented to illustrate the technique.

Paper Nr: 14
Title:

ASSORTMENT OF SOLUTIONS FOR VARIABLE TASKS IN MULTI-OBJECTIVE PROBLEMS

Authors:

Gideon Avigad, Erella Eisenstadt and Uri Ben Hanan

Abstract: In the same manner that species are associated with variants in order to survive, and that human communities, apparently in order to survive, are built up from people with different skills and professions, we suggest in this paper to select a set of diverse solutions in order to optimally solve Multi-Objective Problems (MOPs). As a set, the solutions may cover a wider range of capabilities within the multi-objective space than is possible for an individual member of the set. The diversity within the set is a key issue of this paper and hereinafter designated as an assortment. In the paper, we suggest a computational tool that supports the selection of such an assortment. The selection is posed as an auxiliary MOP of cost versus variability. The cost is directly related to the size of the assortment, whereas the variability is related to the ability of the assortment to cover the objective space. A previously treated problem is adopted and utilized in order to explain and demonstrate the approach.

Paper Nr: 28
Title:

AN EVOLUTIONARY APPROACH FOR ROBUSTNESS TESTING

Authors:

Thaise Yano, Eliane Martins and Fabiano L. de Sousa

Abstract: In this paper we present an evolutionary testing approach to automatically generate robustness test sequences to test a communication protocol, modeled as an extended finite state machine (EFSM). The model represents the normal situation as well as in presence of faults, which makes the model too large to be treated by conventional test case generation approaches, because of the risk of combinatorial explosion. To cope with this problem, we use a testing approach based on test purposes. To search sequences that satisfy the test purposes, we use two evolutionary algorithms: the Generalized Extremal Optimization (GEO) and a Genetic Algorithm (GA). For the moment, only the control flow part of the model is taken into account. Results show that the approach is viable and potentially useful to consider data flow part of complex EFSM models.

Paper Nr: 37
Title:

A PRUNING BASED ANT COLONY ALGORITHM FOR MINIMUM VERTEX COVER PROBLEM

Authors:

Ali D. Mehrabi, Saeed Mehrabi and Abbas Mehrabi

Abstract: Given an undirected, unweighted graph G = (V , E) the minimum vertex cover (MVC) problem is a subset of V whose cardinality is minimum subject to the premise that the selected vertices cover all edges in the graph. In this paper, we propose a meta-heuristic based on Ant Colony Optimization (ACO) approach to find approximate solutions to the minimum vertex cover problem. By introducing a visible set based on pruning paradigm for ants, in each step of their traversal, they are not forced to consider all of the remaining vertices to select the next one for continuing the traversal, resulting very high improvement in both time and convergence rate of the algorithm. We compare our algorithm with two existing algorithms which are based on Genetic Algorithms (GAs) as well as its testing on a variety of benchmarks. Computational experiments evince that the ACO algorithm demonstrates much effectiveness and consistency for solving the minimum vertex cover problem.

Paper Nr: 40
Title:

IMPROVED 2D MAXIMUM ENTROPY THRESHOLD SEGMENTATION METHOD BASED ON PSO

Authors:

Liping Zheng, Guangyao Li, Jing Liang and Quanke Pan

Abstract: Image segmentation plays an important role in the field of image processing. Threshold segmentation is a simple and important method in image segmentation. Maximum Entropy is a common threshold segmentation method. In order to adequately utilize gray information and spatial information of image, an improved 2D entropy computation method is proposed. Otherwise, Particle Swarm Optimization(PSO) algorithm is used to solve maximum of improved entropy. Maximum takes as the optimal image segmentation threshold. In this paper, two CT images were segmented in experiment. Experimental results show that this method can quickly and accurately obtain segmentation threshold. Otherwise, this method has strong anti-noise capability and save computation time.

Paper Nr: 44
Title:

DETECTING LOCALISED MUSCLE FATIGUE DURING ISOMETRIC CONTRACTION USING GENETIC PROGRAMMING

Authors:

Ahmed Kattan, Mohammed Al-Mulla, Francisco Sepulveda and Riccardo Poli

Abstract: We propose the use of Genetic Programming (GP) to generate new features to predict localised muscles fatigue from pre-filtered surface EMG signals. In a training phase, GP evolves programs with multiple components. One component analyses statistical features extracted from EMG to divide the signals into blocks. The blocks’ labels are decided based on the number of zero crossings. These blocks are then projected onto a two-dimensional Euclidean space via two further (evolved) program components. K-means clustering is applied to group similar data blocks. Each cluster is then labelled into one of three types (Fatigue, Transition-to-Fatigue and Non-Fatigue) according to the dominant label among its members. Once a program is evolved that achieves good classification, it can be used on unseen signals without requiring any further evolution. During normal operation the data are again divided into blocks by the first component of the program. The blocks are again projected onto a two-dimensional Euclidean space by the two other components of the program. Finally blocks are labelled according to the k-nearest neighbours. The system alerts the user of possible approaching fatigue once it detects a Transition-to-Fatigue. In experimentation with the proposed technique, the system provides very encouraging results.

Paper Nr: 46
Title:

EVOLUTION STRATEGIES COMPARED TO GENETIC ALGORITHMS IN FINDING OPTIMAL SIGNAL TIMING FOR OVERSATURATED TRANSPORTATION NETWORK

Authors:

Ali Hajbabaie and Rahim F. Benekohal

Abstract: This paper compares the performance of Evolution Strategies (ES) with simple Genetic Algorithms (GAs) in finding optimal or near optimal signal timing in a small network of oversaturated intersections with turning movements. The challenge is to find the green times and the offsets in all intersections so that total vehicle-mile of the network is maximized. By incorporating ES or GA with the micro-simulation package, CORSIM, we have been able to find the near optimal signal timing for the above-mentioned network. The results of this study showed that both algorithms were able to find the near optimal signal timing in the network. For all populations tested in this study, GA yielded higher fitness values than ES. GA with a population size of 300, and selection pressure of 10% produced the highest fitness values. In GA for medium and large size populations, a lower selection pressure produced better results while for small size population a large selection pressure resulted in better fitness values. In ES for small size population, larger µ/λ yielded better results, for medium size population both µ/λ ratios produced similar results, and for large size population smaller µ/λ provided better results.

Paper Nr: 65
Title:

PROBLEM AND ALGORITHM FINE-TUNING - A Case of Study using Bridge Club and Simulated Annealing

Authors:

Nelson Rangel-Valdez, Jose Torres-Jimenez, Josue Bracho-Rios and Pedro Quiz-Ramos

Abstract: Sometimes, it is difficult to cope with a good set of values for the parameters of an algorithm that solves an specific optimization problem. This work presents a methodology for fine-tuning the parameters of a Simulated Annealing (SA) algorithm solving the Bridge Club (BC) problem. The methodology uses Covering Arrays as a tool that evaluates a set of values for the parameters of the SA so that it achieves its best performance when solving BC. The results in the experiments performed show that, using this methodology, the SA reached the optimal solution of the BC problem with a relatively small number of evaluations, in comparison with other strategies that solves BC.

Paper Nr: 69
Title:

BI-OBJECTIVE OPTIMIZATION OF THE PLASMON-ASSISTED LITHOGRAPHY - Design of Plasmonic Nanostructures

Authors:

Caroline Prodhon, Demetrio Macías, Farouk Yalaoui, Alexandre Vial and Lionel Amodeo

Abstract: We discuss the influence of the objective function within the context of plasmons-assisted lithography. From previous publications, numerical experiments have shown that the maximization by means of an Evolutionary Strategy of either the visibility or the contrast of the plasmons interference pattern related to the problem does not lead to the ideal situation in which both criteria are maximal. The idea is then to tackle simultaneously these two objective-functions. However, as they are strongly dependent, a more promising strategy is to focus on the minimal and maximal near-field scattered intensities involved in both previously studied criteria. We think that an Evolutionary Strategy based on a bi-objective optimization of these new criteria will provide more satisfactory solutions with respect to the physical constraints imposed.

Paper Nr: 78
Title:

EVOLVING EFFECTIVE BIDDING FUNCTIONS FOR AUCTION BASED RESOURCE ALLOCATION FRAMEWORK

Authors:

Mohamed Bader-El-Den and Shaheen Fatima

Abstract: In this paper, we present an auction based resource allocation framework. This framework, called GPAuc, uses genetic programming for evolving bidding functions. We describe GPAuc in the context of the exam timetabling problem (ETTP). In the ETTP, there is a set of exams, which must be assigned to a predefined set of slots. Here, the exam time tabling system is the seller that auctions a set of slots. The exams are viewed as the bidding agents in need of slots. The problem is then to find a schedule (i.e., a slot for each exam) such that the total cost of conducting the exams as per the schedule is minimised. In order to arrive at such a schedule, we need to find the bidders’ optimal bids. This is done using genetic programming. The effectiveness of GPAuc is demonstrated experimentally by comparing it with some existing benchmarks for exam time-tabling.

Paper Nr: 93
Title:

NATURAL LANGUAGE AND DIGITAL ENVIRONMENTS - Evolutionary Perspective

Authors:

Oxana Lapteva

Abstract: The mechanisms of language change and variation provide an important input for any system development in the context of its dynamic character, self-organisational aspects and evolution. This work considers a different view on the evolution (in comparison with biological and cultural explanations) and its underlying laws by looking at usage frequency. It aims to explore the mechanisms and driving forces of natural language evolution and link them to the research of Digital Ecosystems involving design and implementation of systems. The frequency-based approach explaining the natural language change and variation can be effectively applied to the evolving formal systems (networks, knowledge spaces, dynamic interfaces, etc.). This paper presents and discusses a simple but very powerful mechanism of evolution: frequency.