Pub Date : 2008-03-04DOI: 10.1109/GEFS.2008.4484559
H. Ishibuchi, Y. Kaisho, Y. Nojima
In various application areas of fuzzy rule-based systems, human users want to know why a particular reasoning result is obtained. That is, fuzzy rule-based systems are required to have high explanation ability. In this paper, we propose an approach to the design of fuzzy rule-based classifiers that can visually explain their classification results to human users. That is, our fuzzy rule-based classifiers can explain to human users why an input pattern is classified as a particular class in an understandable manner. The proposed approach consists of a rule selection method and a visualization interface. Our idea is to design fuzzy rule-based classifiers using fuzzy rules with only two antecedent conditions. A genetic algorithm is employed to construct a compact fuzzy rule-based classifier by choosing only a small number of fuzzy rules. In the classification phase, we use a single winner rule-based method for classifying an input pattern. The classification result of the input pattern is visually explained in a two-dimensional space where the two antecedent conditions of the winner rule are defined. Our approach is compared with feature selection by computational experiments.
{"title":"Designing fuzzy rule-based classifiers that can visually explain their classification results to human users","authors":"H. Ishibuchi, Y. Kaisho, Y. Nojima","doi":"10.1109/GEFS.2008.4484559","DOIUrl":"https://doi.org/10.1109/GEFS.2008.4484559","url":null,"abstract":"In various application areas of fuzzy rule-based systems, human users want to know why a particular reasoning result is obtained. That is, fuzzy rule-based systems are required to have high explanation ability. In this paper, we propose an approach to the design of fuzzy rule-based classifiers that can visually explain their classification results to human users. That is, our fuzzy rule-based classifiers can explain to human users why an input pattern is classified as a particular class in an understandable manner. The proposed approach consists of a rule selection method and a visualization interface. Our idea is to design fuzzy rule-based classifiers using fuzzy rules with only two antecedent conditions. A genetic algorithm is employed to construct a compact fuzzy rule-based classifier by choosing only a small number of fuzzy rules. In the classification phase, we use a single winner rule-based method for classifying an input pattern. The classification result of the input pattern is visually explained in a two-dimensional space where the two antecedent conditions of the winner rule are defined. Our approach is compared with feature selection by computational experiments.","PeriodicalId":343300,"journal":{"name":"2008 3rd International Workshop on Genetic and Evolving Systems","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124999910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-03-04DOI: 10.1109/GEFS.2008.4484564
E. Lughofer, C. Guardiola
The main contribution of this paper is a novel fault detection strategy, which is able to cope with changing system states at on-line measurement systems fully automatically. For doing so, an improved fault detection logic is introduced which is based on data-driven evolving fuzzy models. These are sample-wise trained from online measurement data, i.e. the structure and rules of the models evolve over time in order to cope 1.) with high-frequented measurement recordings and 2.) online changing operating conditions. The evolving fuzzy models represent (changing) non-linear dependencies between certain system variables and are used for calculating the deviation between expected model outputs and real measured values on new incoming data samples (rarr residuals). The residuals are compared with local confidence regions surrounding the evolving fuzzy models, so-called local error bars, incrementally calculated synchronously to the models. The behavior of the residuals is analyzed over time by an adaptive univariate statistical approach. Evaluation results based on high-dimensional measurement data from engine test benches are demonstrated at the end of the paper, where the novel fault detection approach is compared against static analytical (fault) models.
{"title":"Applying evolving fuzzy models with adaptive local error bars to on-line fault detection","authors":"E. Lughofer, C. Guardiola","doi":"10.1109/GEFS.2008.4484564","DOIUrl":"https://doi.org/10.1109/GEFS.2008.4484564","url":null,"abstract":"The main contribution of this paper is a novel fault detection strategy, which is able to cope with changing system states at on-line measurement systems fully automatically. For doing so, an improved fault detection logic is introduced which is based on data-driven evolving fuzzy models. These are sample-wise trained from online measurement data, i.e. the structure and rules of the models evolve over time in order to cope 1.) with high-frequented measurement recordings and 2.) online changing operating conditions. The evolving fuzzy models represent (changing) non-linear dependencies between certain system variables and are used for calculating the deviation between expected model outputs and real measured values on new incoming data samples (rarr residuals). The residuals are compared with local confidence regions surrounding the evolving fuzzy models, so-called local error bars, incrementally calculated synchronously to the models. The behavior of the residuals is analyzed over time by an adaptive univariate statistical approach. Evaluation results based on high-dimensional measurement data from engine test benches are demonstrated at the end of the paper, where the novel fault detection approach is compared against static analytical (fault) models.","PeriodicalId":343300,"journal":{"name":"2008 3rd International Workshop on Genetic and Evolving Systems","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121540983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-03-04DOI: 10.1109/GEFS.2008.4484562
W. Brockmann, N. Rosemann
In the field of self-optimizing automation systems, incremental local learning is an important technique. But especially in case of closed loop coupling, learnt anomalies may have a negative influence on the entire future of the evolving system. In the worst case, this may result in unstable or chaotic system behavior. Thus it is crucial to detect anomalies in online learning systems instantaneously to be able to take immediate counteractions. This paper presents an intuitive approach how to detect anomalies in incrementally and locally learning TS-fuzzy systems by looking at local meta-level characteristics of the learnt function. The practical feasibility of this approach is then investigated in experiments with a real pole-balancing cart.
{"title":"Instantaneous anomaly detection in online learning fuzzy systems","authors":"W. Brockmann, N. Rosemann","doi":"10.1109/GEFS.2008.4484562","DOIUrl":"https://doi.org/10.1109/GEFS.2008.4484562","url":null,"abstract":"In the field of self-optimizing automation systems, incremental local learning is an important technique. But especially in case of closed loop coupling, learnt anomalies may have a negative influence on the entire future of the evolving system. In the worst case, this may result in unstable or chaotic system behavior. Thus it is crucial to detect anomalies in online learning systems instantaneously to be able to take immediate counteractions. This paper presents an intuitive approach how to detect anomalies in incrementally and locally learning TS-fuzzy systems by looking at local meta-level characteristics of the learnt function. The practical feasibility of this approach is then investigated in experiments with a real pole-balancing cart.","PeriodicalId":343300,"journal":{"name":"2008 3rd International Workshop on Genetic and Evolving Systems","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115105149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-03-04DOI: 10.1109/GEFS.2008.4484573
J. Casillas, A. Orriols-Puig, Ester Bernadó-Mansilla
This paper proposes Pitts-DNF-C, a multi- objective Pittsburgh-style Learning Classifier System that evolves a set of DNF-type fuzzy rules for classification tasks. The system is explicitly designed to only explore solutions that lead to consistent, complete, and compact rule sets without redundancies and inconsistencies. The behavior of the system is analyzed on a collection of real-world data sets, showing its competitiveness in terms of performance and interpretability with respect to three other fuzzy learners.
{"title":"Toward evolving consistent, complete, and compact fuzzy rule sets for classification problems","authors":"J. Casillas, A. Orriols-Puig, Ester Bernadó-Mansilla","doi":"10.1109/GEFS.2008.4484573","DOIUrl":"https://doi.org/10.1109/GEFS.2008.4484573","url":null,"abstract":"This paper proposes Pitts-DNF-C, a multi- objective Pittsburgh-style Learning Classifier System that evolves a set of DNF-type fuzzy rules for classification tasks. The system is explicitly designed to only explore solutions that lead to consistent, complete, and compact rule sets without redundancies and inconsistencies. The behavior of the system is analyzed on a collection of real-world data sets, showing its competitiveness in terms of performance and interpretability with respect to three other fuzzy learners.","PeriodicalId":343300,"journal":{"name":"2008 3rd International Workshop on Genetic and Evolving Systems","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123876380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-03-04DOI: 10.1109/GEFS.2008.4484561
N. S. Milani, Alireza Kashanipour, A. R. Kashanipour
In this paper we propose a color classification algorithm in which an evolutionary design optimizes a fuzzy system for color classification and image segmentation. This system works with the least number of rules and has minimum error rate by the mean of particle swarm optimization (PSO) method. In this approach each particle of the swarm codes a set of fuzzy rules. During evolution, each member of a population tries to maximize a fitness criterion which has designed to raise classification rate and to reduce the number of rules. Finally, the particle with the highest fitness value is selected as the best set of fuzzy rules for image segmentation. Fuzzy sets are defined on the H, S and L components of the HSL Color Space to provide a fuzzy logic model which aims to follow the human intuition of Color Classification. Color-based vision applications face the challenge of color variations by illumination. The final system designed by this method is adaptive to continuous variable lighting according to its evolving-fuzzy nature. In this method parameters setting is done only once .The experimental results in RoboCup leagues demonstrate that the presented approach can be very robust to noise and light variations.
{"title":"Evolving fuzzy classifier system using PSO for RoboCup vision applications","authors":"N. S. Milani, Alireza Kashanipour, A. R. Kashanipour","doi":"10.1109/GEFS.2008.4484561","DOIUrl":"https://doi.org/10.1109/GEFS.2008.4484561","url":null,"abstract":"In this paper we propose a color classification algorithm in which an evolutionary design optimizes a fuzzy system for color classification and image segmentation. This system works with the least number of rules and has minimum error rate by the mean of particle swarm optimization (PSO) method. In this approach each particle of the swarm codes a set of fuzzy rules. During evolution, each member of a population tries to maximize a fitness criterion which has designed to raise classification rate and to reduce the number of rules. Finally, the particle with the highest fitness value is selected as the best set of fuzzy rules for image segmentation. Fuzzy sets are defined on the H, S and L components of the HSL Color Space to provide a fuzzy logic model which aims to follow the human intuition of Color Classification. Color-based vision applications face the challenge of color variations by illumination. The final system designed by this method is adaptive to continuous variable lighting according to its evolving-fuzzy nature. In this method parameters setting is done only once .The experimental results in RoboCup leagues demonstrate that the presented approach can be very robust to noise and light variations.","PeriodicalId":343300,"journal":{"name":"2008 3rd International Workshop on Genetic and Evolving Systems","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133607982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-03-04DOI: 10.1109/GEFS.2008.4484563
I. Kallel, S. Mezghani, A. Alimi
Referring to our readings about evolving and adaptive agents, we notice that most researchers proclaim the adaptivity of their systems' entities but without being able to estimate or evaluate it in a measure. Throughout this paper, we propose at first, to specify some crucial characteristics qualifying an entity (or agent) as evolving and adaptive. Since these characteristics are generally imperfect and suffer from uncertainties and inaccuracies, we propose a fuzzy rule base system (FRBS) as an intelligent method in order to estimate the measure of an adaptivity degree. We detail the fuzzy definition of selected inputs and output. Finally, we test and discuss the reliability of the suggested method on several examples, got from published works in various fields and had different characteristics.
{"title":"Towards a fuzzy evaluation of the adaptivity degree of an evolving agent","authors":"I. Kallel, S. Mezghani, A. Alimi","doi":"10.1109/GEFS.2008.4484563","DOIUrl":"https://doi.org/10.1109/GEFS.2008.4484563","url":null,"abstract":"Referring to our readings about evolving and adaptive agents, we notice that most researchers proclaim the adaptivity of their systems' entities but without being able to estimate or evaluate it in a measure. Throughout this paper, we propose at first, to specify some crucial characteristics qualifying an entity (or agent) as evolving and adaptive. Since these characteristics are generally imperfect and suffer from uncertainties and inaccuracies, we propose a fuzzy rule base system (FRBS) as an intelligent method in order to estimate the measure of an adaptivity degree. We detail the fuzzy definition of selected inputs and output. Finally, we test and discuss the reliability of the suggested method on several examples, got from published works in various fields and had different characteristics.","PeriodicalId":343300,"journal":{"name":"2008 3rd International Workshop on Genetic and Evolving Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129919124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-03-04DOI: 10.1109/GEFS.2008.4484569
M. Montazeri-Gh, A. Ahmadi, M. Asadi
This paper presents a genetic-fuzzy approach for hybrid electric vehicle control based on driving pattern recognition and prediction. In this approach, data collection in the real traffic conditions is employed for classification of several driving patterns. These driving patterns represent different traffic conditions e.g. congested, urban and so on. The analysis used for the driving pattern recognition is based on the definition of microtrips. In addition, a Markov chain modeling is used for traffic condition prediction based on the modeling of probability of the sequence of microtrips. The driving pattern prediction is then utilized for optimization of the HEV control parameters using a genetic-fuzzy approach. In this approach, a fuzzy logic controller (FLC) is designed to be intelligent so as to manage the internal combustion engine (ICE) to work in the vicinity of its optimal condition. The fuzzy membership function parameters are then tuned using the genetic algorithm (GA). Finally, simulation results are presented to show the effectiveness of the approach for reducing the HEV fuel consumption and emissions.
{"title":"Driving condition recognition for genetic-fuzzy HEV Control","authors":"M. Montazeri-Gh, A. Ahmadi, M. Asadi","doi":"10.1109/GEFS.2008.4484569","DOIUrl":"https://doi.org/10.1109/GEFS.2008.4484569","url":null,"abstract":"This paper presents a genetic-fuzzy approach for hybrid electric vehicle control based on driving pattern recognition and prediction. In this approach, data collection in the real traffic conditions is employed for classification of several driving patterns. These driving patterns represent different traffic conditions e.g. congested, urban and so on. The analysis used for the driving pattern recognition is based on the definition of microtrips. In addition, a Markov chain modeling is used for traffic condition prediction based on the modeling of probability of the sequence of microtrips. The driving pattern prediction is then utilized for optimization of the HEV control parameters using a genetic-fuzzy approach. In this approach, a fuzzy logic controller (FLC) is designed to be intelligent so as to manage the internal combustion engine (ICE) to work in the vicinity of its optimal condition. The fuzzy membership function parameters are then tuned using the genetic algorithm (GA). Finally, simulation results are presented to show the effectiveness of the approach for reducing the HEV fuel consumption and emissions.","PeriodicalId":343300,"journal":{"name":"2008 3rd International Workshop on Genetic and Evolving Systems","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127118675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-03-04DOI: 10.1109/GEFS.2008.4484567
I. Walter, F. Gomide
Following the development of online markets, trading practices as dynamic pricing, online auctions and exchanges have become relevant to a variety of markets. In this paper we suggest a machine learning approach to find a suitable bidding strategy for an auction participant using information commonly available in online auction settings. We take the electricity auction as the main application example, due to its importance as an experimental instance of the suggested approach. In previous works we evolved successful fuzzy bidding strategies. Here we introduce a coevolutionary algorithm to study how the evolving strategies react to each other in a more dynamic environment. By enabling a fuzzy system to learn trough an evolutionary algorithm one expects to find effective and transparent bidding strategies. By adopting a coevolutionary approach a more realistic representation of the agents participating in an auction based electricity market allows the evolutionary bidding strategies interact. The results show that the coevolutionary approach can improve agents profits at the cost of increasing system hourly price paid by demand.
{"title":"Coevolutionary fuzzy multiagent bidding strategies in competitive electricity markets","authors":"I. Walter, F. Gomide","doi":"10.1109/GEFS.2008.4484567","DOIUrl":"https://doi.org/10.1109/GEFS.2008.4484567","url":null,"abstract":"Following the development of online markets, trading practices as dynamic pricing, online auctions and exchanges have become relevant to a variety of markets. In this paper we suggest a machine learning approach to find a suitable bidding strategy for an auction participant using information commonly available in online auction settings. We take the electricity auction as the main application example, due to its importance as an experimental instance of the suggested approach. In previous works we evolved successful fuzzy bidding strategies. Here we introduce a coevolutionary algorithm to study how the evolving strategies react to each other in a more dynamic environment. By enabling a fuzzy system to learn trough an evolutionary algorithm one expects to find effective and transparent bidding strategies. By adopting a coevolutionary approach a more realistic representation of the agents participating in an auction based electricity market allows the evolutionary bidding strategies interact. The results show that the coevolutionary approach can improve agents profits at the cost of increasing system hourly price paid by demand.","PeriodicalId":343300,"journal":{"name":"2008 3rd International Workshop on Genetic and Evolving Systems","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132964346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-03-04DOI: 10.1109/GEFS.2008.4484571
D. Stavrakoudis, A. K. Papastamoulis, Ioannis B. Theocharis
An enhanced memory TSK-type recurrent fuzzy network (EM-TRFN) is proposed in this paper, for dynamic control of nonlinear systems. The network employs feedback connections in the rule layer, with their synaptic links being implemented through finite impulse response (FIR) filters. Thus, the network structure is enriched in terms of past information processing capabilities. Both structure and parameter learning are performed through a hybrid evolutionary algorithm, with its representation scheme employing variable-length mixed-type chromosomes. Comparative results in a control problem of a dynamic system prove the EM-TRFN's structural merits, as well as the proposed learning algorithm's ability in dealing with complex search spaces.
{"title":"Evolutionary identification of a recurrent fuzzy neural network with enhanced memory capabilities","authors":"D. Stavrakoudis, A. K. Papastamoulis, Ioannis B. Theocharis","doi":"10.1109/GEFS.2008.4484571","DOIUrl":"https://doi.org/10.1109/GEFS.2008.4484571","url":null,"abstract":"An enhanced memory TSK-type recurrent fuzzy network (EM-TRFN) is proposed in this paper, for dynamic control of nonlinear systems. The network employs feedback connections in the rule layer, with their synaptic links being implemented through finite impulse response (FIR) filters. Thus, the network structure is enriched in terms of past information processing capabilities. Both structure and parameter learning are performed through a hybrid evolutionary algorithm, with its representation scheme employing variable-length mixed-type chromosomes. Comparative results in a control problem of a dynamic system prove the EM-TRFN's structural merits, as well as the proposed learning algorithm's ability in dealing with complex search spaces.","PeriodicalId":343300,"journal":{"name":"2008 3rd International Workshop on Genetic and Evolving Systems","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125882356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-03-04DOI: 10.1109/GEFS.2008.4484568
S. Guadarrama, Ruben Vazquez
In this paper, we design, build and tune a fuzzy rule-based car controller for FUZZ-IEEE 2007 Car Racing Competition. The membership functions of the car controller are tuned with coevolutionary genetic algorithms. Cooperative and competitive approaches to tuning parameters are compared. In principle, results obtained with a cooperative approach with a BLX cross operator are slightly better than results derived from a competitive method with a 1-point cross operator. In any case, further experiments are needed to support our findings.
{"title":"Tuning a fuzzy racing car by coevolution","authors":"S. Guadarrama, Ruben Vazquez","doi":"10.1109/GEFS.2008.4484568","DOIUrl":"https://doi.org/10.1109/GEFS.2008.4484568","url":null,"abstract":"In this paper, we design, build and tune a fuzzy rule-based car controller for FUZZ-IEEE 2007 Car Racing Competition. The membership functions of the car controller are tuned with coevolutionary genetic algorithms. Cooperative and competitive approaches to tuning parameters are compared. In principle, results obtained with a cooperative approach with a BLX cross operator are slightly better than results derived from a competitive method with a 1-point cross operator. In any case, further experiments are needed to support our findings.","PeriodicalId":343300,"journal":{"name":"2008 3rd International Workshop on Genetic and Evolving Systems","volume":"206 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133262661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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