Author: Community

Improving the performance of evolutionary algorithms in grid-based puzzles resolution

Abstract  This paper proposes several modifications to existing hybrid evolutionary algorithms in grid-based puzzles, using a-priori
probabilities of 0/1 occurrence in binary encodings. This calculation of a-priori probabilities of bits is p…

Abstract  This paper proposes several modifications to existing hybrid evolutionary algorithms in grid-based puzzles, using a-priori
probabilities of 0/1 occurrence in binary encodings. This calculation of a-priori probabilities of bits is possible in grid-based
problems (puzzles in this case) due to their special structure, with the solution confined into a grid. The work is focused
in two different grid-based puzzles, the Japanese puzzles and the Light-up puzzle, each one having special characteristics
in terms of constraints, which must be taken into account for the probabilities of bit calculation. For these puzzles, we
show the process of a-priori probabilities calculation, and we modify the initialization of the EAs to improve their performance.
We also include novel mutation operators based on a-priori probabilities, which makes more effective the evolutionary search
of the algorithms in the tackled puzzles. The performance of the algorithms with these new initialization and novel mutation
operators is compared with the performance without them. We show that the new initialization and operators based on a-priori
probabilities of bits make the evolutionary search more effective and also improve the scalability of the algorithms.

  • Content Type Journal Article
  • DOI 10.1007/s12065-009-0030-3
  • Authors
    • E. G. Ortiz-García, Universidad de Alcalá, Escuela Politécnica Superior Department of Signal Theory and Communications Alcalá de Henares 28871 Madrid Spain
    • S. Salcedo-Sanz, Universidad de Alcalá, Escuela Politécnica Superior Department of Signal Theory and Communications Alcalá de Henares 28871 Madrid Spain
    • Á. M. Pérez-Bellido, Universidad de Alcalá, Escuela Politécnica Superior Department of Signal Theory and Communications Alcalá de Henares 28871 Madrid Spain
    • L. Carro-Calvo, Universidad de Alcalá, Escuela Politécnica Superior Department of Signal Theory and Communications Alcalá de Henares 28871 Madrid Spain
    • A. Portilla-Figueras, Universidad de Alcalá, Escuela Politécnica Superior Department of Signal Theory and Communications Alcalá de Henares 28871 Madrid Spain
    • X. Yao, The University of Birmingham The Centre for Research in Computational Intelligence and Applications (CERCIA), School of Computer Science Birmingham UK

Boundedness analysis for open Chemical Reaction Networks with mass-action kinetics

Abstract  This paper describes the working principles of an algorithm for boundedness analysis of open Chemical Reaction Networks endowed
with mass-action kinetics. Such models can be thought of both as a special class of compartmental syste…

Abstract  

This paper describes the working principles of an algorithm for boundedness analysis of open Chemical Reaction Networks endowed
with mass-action kinetics. Such models can be thought of both as a special class of compartmental systems or a particular
type of continuous Petri Nets, in which the firing rates of transitions are not constant or preassigned, but expressed as
a function of the continuous marking of the network (function which in chemistry is referred to as the “kinetics”). The algorithm
can be applied to a broad class of such open networks, and returns, as an outcome, a classification of the possible dynamical
behaviors that are compatible with the network structure, by classifying each variable either as bounded, converging to 0
or diverging to ∞. This can be viewed as a qualitative study of Input–Output Stability for chemical networks, or more precisely,
as a classification of its possible I–O instability patterns. Our goal is to analyze the system irrespectively of values of
kinetic parameters. More precisely, we attempt to analyze it simultaneously for all possible values. Remarkably, tests on
non-trivial examples (one of which is discussed in this paper) showed that, as the kinetic constants of the network are varied,
all the compatible behaviors could be observed in simulations. Finally, we discuss and illustrate how the results relate to
previous works on the qualitative dynamics of closed reaction networks.

  • Content Type Journal Article
  • Pages 751-774
  • DOI 10.1007/s11047-009-9163-7
  • Authors
    • David Angeli, Dipartimento di Sistemi e Informatica, University of Florence, Florence, Italy

A synthetic genetic circuit whose signal-response curve is temperature-tunable from band-detection to sigmoidal behaviour

Abstract  Programming new cellular functions by using synthetic gene circuits is a key goal in synthetic biology, and an important element
of this process is the ability to couple to the information processing systems of the host cell using …

Abstract  Programming new cellular functions by using synthetic gene circuits is a key goal in synthetic biology, and an important element
of this process is the ability to couple to the information processing systems of the host cell using synthetic systems with
various signal-response characteristics. Here, we present a synthetic gene system in Escherichia coli whose signal-response curve may be tuned from band detection (strongest response within a band of input concentrations) to
a switch-like sigmoidal response, simply by altering the temperature. This change from a band-detection response to a sigmoidal
response has not previously been implemented. The system allows investigation of a range of signal-response behaviours with
minimal effort: a single system, once inserted into the cells, provides a range of response curves without any genetic alterations
or replacement with other systems. By altering its output, the system may couple to other synthetic or natural genetic circuits,
and thus serve as a useful modular component. A mathematical model has also been developed which captures the essential qualitative
behaviours of the circuit.

  • Content Type Journal Article
  • DOI 10.1007/s11047-009-9167-3
  • Authors
    • Sangram Bagh, University of Toronto Mississauga Department of Chemical and Physical Sciences, Institute for Optical Sciences 3359 Mississauga Rd. N Mississauga ON L5L 1C6 Canada
    • David R. McMillen, University of Toronto Mississauga Department of Chemical and Physical Sciences, Institute for Optical Sciences 3359 Mississauga Rd. N Mississauga ON L5L 1C6 Canada

Evolutionary synthesis of low-sensitivity digital filters using adjacency matrix

Abstract  An evolutionary synthesis method to generate digital filters with low coefficient sensitivity is presented. The method uses
a chromosome coding based on the graph adjacency matrix representation. It is shown that the proposed chrom…

Abstract  An evolutionary synthesis method to generate digital filters with low coefficient sensitivity is presented. The method uses
a chromosome coding based on the graph adjacency matrix representation. It is shown that the proposed chromosome representation
enables to easily verify and avoid the generation of topologically invalid and non-computable individuals during the evolutionary
process. The efficiency of the proposed algorithm is tested in the synthesis of two low-pass digital filters and the results
are compared with other examples found in the literature.

  • Content Type Journal Article
  • DOI 10.1007/s12065-009-0028-x
  • Authors
    • Leonardo Bruno de Sá, Brazilian Army Technological Center Av das Américas, 28705, Guaratiba Rio de Janeiro 23020-470 Brazil
    • Antonio Mesquita, Federal University of Rio de Janeiro Rio de Janeiro Brazil

Rule base and adaptive fuzzy operators cooperative learning of Mamdani fuzzy systems with multi-objective genetic algorithms

Abstract  In this paper, we present an evolutionary multi-objective learning model achieving cooperation between the rule base and the
adaptive fuzzy operators of the inference system in order to obtain simpler, more compact and still accura…

Abstract  In this paper, we present an evolutionary multi-objective learning model achieving cooperation between the rule base and the
adaptive fuzzy operators of the inference system in order to obtain simpler, more compact and still accurate linguistic fuzzy
models by learning fuzzy inference adaptive operators together with rules. The multi-objective evolutionary algorithm proposed
generates a set of fuzzy rule based systems with different trade-offs between interpretability and accuracy, allowing the
designers to select the one that involves the most suitable balance for the desired application. We develop an experimental
study testing our approach with some variants on nine real-world regression datasets finding the advantages of cooperative
compared to sequential models, as well as multi-objective compared with single-objective models. The study is elaborated comparing
different approaches by applying non-parametric statistical tests for pair-wise. Results confirm the usefulness of the proposed
approach.

  • Content Type Journal Article
  • DOI 10.1007/s12065-009-0026-z
  • Authors
    • Antonio A. Márquez, University of Huelva Information Technologies Department Huelva Spain
    • Francisco A. Márquez, University of Huelva Information Technologies Department Huelva Spain
    • Antonio Peregrín, University of Huelva Information Technologies Department Huelva Spain

Extending a simple genetic cooperative-competitive learning fuzzy classifier to low quality datasets

Abstract  Exploiting the information in low quality datasets has been recently acknowledged as a new challenge in Genetic Fuzzy Systems.
Owing to this, in this paper we discuss the basic principles that govern the extension of a fuzzy rule b…

Abstract  Exploiting the information in low quality datasets has been recently acknowledged as a new challenge in Genetic Fuzzy Systems.
Owing to this, in this paper we discuss the basic principles that govern the extension of a fuzzy rule based classifier to
interval and fuzzy data. We have also applied these principles to the genetic learning of a simple cooperative-competitive
algorithm, that becomes the first example of a Genetic Fuzzy Classifier able to use low quality data. Additionally, we introduce
a benchmark, comprising some synthetic samples and two real-world problems that involve interval and fuzzy-valued data, that
can be used to assess future algorithms of the same kind.

  • Content Type Journal Article
  • DOI 10.1007/s12065-009-0024-1
  • Authors
    • Ana M. Palacios, Universidad de Oviedo Departamento de Informática 33071 Gijón Asturias Spain
    • Luciano Sánchez, Universidad de Oviedo Departamento de Informática 33071 Gijón Asturias Spain
    • Inés Couso, Universidad de Oviedo Departamento de Estadística e I.O. y D.M 33071 Gijón Asturias Spain

Evolution of visual controllers for obstacle avoidance in mobile robotics

Abstract  The purpose of this work is to automatically design vision algorithms for a mobile robot, adapted to its current visual context.
In this paper we address the particular task of obstacle avoidance using monocular vision. Starting fr…

Abstract  The purpose of this work is to automatically design vision algorithms for a mobile robot, adapted to its current visual context.
In this paper we address the particular task of obstacle avoidance using monocular vision. Starting from a set of primitives
composed of the different techniques found in the literature, we propose a generic structure to represent the algorithms,
using standard resolution video sequences as an input, and velocity commands to control a wheel robot as an output. Grammar
rules are then used to construct correct instances of algorithms, that are then evaluated using different protocols: evaluation
of trajectories performed in a goal reaching task, or imitation of a hand-guided trajectory. A genetic program is applied
to evolve populations of algorithms in order to optimize the performances of the controllers. The first results obtained in
a simulated environment show that the evolution produces algorithms that can be easily interpreted and which are clearly adapted
to the visual context. However, the resulting trajectories are often erratic, and the generalization capacities are poor.
To improve the results, we propose to use a two-phase evolution combining imitation and goal reaching evaluations, and to
add some constraints in the grammar rules to enforce a more generic behavior. The results obtained in simulation show that
the evolved algorithms are more efficient and more generic. Finally, we apply the imitation based evolution on real sequences
and test the evolved algorithms on a real robot. Though simplified by dropping the goal reaching constraint, the resulting
algorithms behave well in a corridor centering task, and show certain generalization capacities.

  • Content Type Journal Article
  • DOI 10.1007/s12065-009-0021-4
  • Authors
    • Renaud Barate, ENSTA 32 Bd Victor 75739 Paris Cedex 15 France
    • Antoine Manzanera, ENSTA 32 Bd Victor 75739 Paris Cedex 15 France

Introduction: special issue on parallel and distributed evolutionary algorithms, part I

Introduction: special issue on parallel and distributed evolutionary algorithms, part I
Content Type Journal ArticlePages 339-341DOI 10.1007/s10710-009-9094-1Authors
Marco Tomassini, University of Lausanne Information Systems Department, HEC Lausann…

Introduction: special issue on parallel and distributed evolutionary algorithms, part I

  • Content Type Journal Article
  • Pages 339-341
  • DOI 10.1007/s10710-009-9094-1
  • Authors
    • Marco Tomassini, University of Lausanne Information Systems Department, HEC Lausanne Switzerland
    • Leonardo Vanneschi, University of Milano-Bicocca Department of Informatics, Systems and Communication (D.I.S.Co.) Milan Italy

Special issue on genetic fuzzy systems: new advances

Special issue on genetic fuzzy systems: new advances
Content Type Journal ArticleDOI 10.1007/s12065-009-0027-yAuthors
Rafael Alcalá, University of Granada Department of Computer Science and A.I. 18071 Granada SpainYusuke Nojima, Osaka Prefecture Un…

Special issue on genetic fuzzy systems: new advances

  • Content Type Journal Article
  • DOI 10.1007/s12065-009-0027-y
  • Authors
    • Rafael Alcalá, University of Granada Department of Computer Science and A.I. 18071 Granada Spain
    • Yusuke Nojima, Osaka Prefecture University Department of Computer Science and Intelligent Systems 1-1 Gakuen-cho, Naka-ku Sakai, Osaka 599-8531 Japan

Multiagent coevolutionary genetic fuzzy system to develop bidding strategies in electricity markets: computational economics to assess mechanism design

Abstract  This paper suggests a genetic fuzzy system approach to develop bidding strategies for agents in online auction environments.
Assessing efficient bidding strategies is a key to evaluate auction models and verify if the underlying me…

Abstract  This paper suggests a genetic fuzzy system approach to develop bidding strategies for agents in online auction environments.
Assessing efficient bidding strategies is a key to evaluate auction models and verify if the underlying mechanism design achieves
its intended goals. Due to its relevance in current energy markets worldwide, we use day-ahead electricity auctions as an
experimental and application instance of the approach developed in this paper. Successful fuzzy bidding strategies have been
developed by genetic fuzzy systems using coevolutionary algorithms. In this paper we address a coevolutionary fuzzy system
algorithm and present recent results concerning bidding strategies behavior. Coevolutionary approaches developed by coevolutionary
agents interact through their fuzzy bidding strategies in a multiagent environment and allow realistic and transparent representations
of agents behavior in auction-based markets. They also improve market representation and evaluation mechanisms. In particular,
we study how the coevolutionary fuzzy bidding strategies perform against each other during hourly electric energy auctions.
Experimental results show that coevolutionary agents may enhance their profits at the cost of increasing system hourly price
paid by demand.

  • Content Type Journal Article
  • DOI 10.1007/s12065-009-0023-2
  • Authors
    • Igor Walter, Brazilian Electricity Regulatory Agency, ANEEL Brasília DF Brazil
    • Fernando Gomide, University of Campinas, Unicamp Faculty of Electrical and Computer Engineering, FEEC Campinas SP Brazil