This report describes the solution of NiSIS Competition 2006 awarded by "best nature inspired con... more This report describes the solution of NiSIS Competition 2006 awarded by "best nature inspired concept" award.
The Social Impact Theory based Optimizer (SITO) is a novel population-based binary optimization m... more The Social Impact Theory based Optimizer (SITO) is a novel population-based binary optimization metaheuristic, inspired by computer simulation of Dynamic Theory of Social Impact. Its first version has been developed in 2006. Recently, we can summarize some of most important aspects of SITO, including some ideas for future work.
In nature, industry, medicine, social environment, simply everywhere we find a lot of data that b... more In nature, industry, medicine, social environment, simply everywhere we find a lot of data that bear certain information. A dictionary defines data as facts or figures from which conclusions may be drawn. Data can be classified as either numeric or nonnumeric. The structure and nature of data greatly affects the choice of analysis method. Under the term structure we understand the facts that the data might be not a single number but n-tuples of measurements. Structure is also very closely linked to the reason of data collection and method of measurement. The discussion is focused on the similarities and differences of nature inspired methods and their natural counterparts in light of continuous and discrete properties. Different examples of nature inspired methods will be inspected in terms of data, problem domains and inner structure and principles.
A robust, automated classification system for polysomnographic (PSG) data targeted to the newborn... more A robust, automated classification system for polysomnographic (PSG) data targeted to the newborn sleep stage identification is presented. The problem of polysomnographic signal classification is very often difficult because of artifacts and noise. Furthermore, for each signal, a special classification method for each particular type of segment must be mostly used. This paper proposes fully unsupervised approach using adaptive segmentation, appropriate features extraction and hierarchical clustering (Ward’s minimumvariance method is used). The mutual information concept was applied to results of hierarchical clustering. The proposed procedure was tested on real neonatal data. All sleep states were successfully separated by a combination of EEG, EMG, EOG, PNG and ECG channels.
Robot path planning problem is one of most important task mobile robots. This paper proposes an o... more Robot path planning problem is one of most important task mobile robots. This paper proposes an original approach using a path description by string of cubic splines. Such path is easy executable and natural for car-like robot. Furthermore, it is possible to ensure smooth derivation in connections of particular splines. In this case, the path planning is equivalent to optimization of parameters of splines. An evolutionary technique called particle swarm optimization (PSO) was used hereunder due to its relatively fast convergence and global search character. Various settings of PSO parameters were tested and the best setting was compared to two classical mobile robot path planning algorithms.
The main goal of the study is an unsupervised classification of school children dyslexia. Eye mov... more The main goal of the study is an unsupervised classification of school children dyslexia. Eye movements of 49 female subjects were measured using videooculographic technique (VOG) during two non-reading and one reading tasks. A feature selection was performed obtaining data set consisting of 26 features. Next an inductive modelling technique was applied to data set resulting in extraction of six features which were used as the input to self-organizing map (SOM). Three clusters were finally formed by the SOM proving that the proposed methodology is suitable for automatic dyslexia analysis.
Biological processes and methods have been influencing science and technology for many decades. T... more Biological processes and methods have been influencing science and technology for many decades. The ideas of feedback and control processes Norbert Wiener used in his cybernetics were based on observation of these phenomena in biological systems. Artificial intelligence and intelligent systems have been fundamentally interested in the phenomenology of living systems, namely perception, decision-making, action, and learning. Natural systems exhibit many properties that form fundamentals for a number of nature inspired applications -dynamics, flexibility, robustness, self-organisation, simplicity of basic elements, and decentralization. This paper reviews examples of nature inspired software applications, mostly drawing inspiration from collective behaviour of social colonies.
This paper presents new application of Particle Swarm Optimization (PSO) algorithm for training H... more This paper presents new application of Particle Swarm Optimization (PSO) algorithm for training Hidden Markov Models (HMMs). The problem of finding an optimal set of model parameters is numerical optimization problem constrained by stochastic character of HMM parameters. Constraint handling is carried out using three different ways and the results are compared to Baum-Welch algorithm (BW), commonly used for HMM training. The global searching PSO method is much less sensitive to local extremes and finds better solutions than the local BW algorithm, which often converges to local optima. The advantage of PSO approach was markedly evident, when longer training sequence was used.
Biological processes and methods have been influencing science and technology for many decades. T... more Biological processes and methods have been influencing science and technology for many decades. The ideas of feedback and control processes Norbert Wiener used in his cybernetics were based on observation of these phenomena in biological systems. Artificial intelligence and intelligent systems have been fundamentally interested in the phenomenology of living systems, namely perception, decision-making, action, and learning. Natural systems exhibit many properties that form fundamentals for a number of nature inspired applications – dynamics, flexibility, robustness, self-organisation, simplicity of basic elements, and decentralization. This paper reviews examples of nature inspired software applications focused on optimization problems, mostly drawing inspiration from collective behaviour of social colonies.
This paper introduces a novel stochastic and population-based binary optimization method inspired... more This paper introduces a novel stochastic and population-based binary optimization method inspired by social psychology. It is called Social Impact Theory based Optimization (SITO). The method has been developed with the use of some simple modifications of simulations of Latané’s Dynamic Social Impact Theory. The usability of the algorithm is demonstrated via experimental testing on some test problems. The results showed that the initial version of SITO performs comparably to the simple Genetic Algorithm (GA) and the binary Particle Swarm Optimization (bPSO).
The interactions taking place in the society could be a source of rich inspiration for the develo... more The interactions taking place in the society could be a source of rich inspiration for the development of novel computational methods. This paper describes an application of two optimization methods based on the idea of social interactions. The first one is the Social Impact Theory based Optimizer - a novel method directly inspired by and based on the Dynamic Theory of Social Impact known from social psychology. The second one is the binary Particle Swarm Optimization - well known optimization technique, which could be understood as to be inspired by decision making process in a group. The two binary optimization methods are applied in the area of automatic pattern classification to selection of an optimal subset of classifier’s inputs. The testing is performed using four datasets from UCI repository. The results show the ability of both methods to significantly reduce input dimensionality and simultaneously keep up the generalization ability.
In this paper we present application of various versions of the particle swarm optimization metho... more In this paper we present application of various versions of the particle swarm optimization method (PSO) in the process of generation of multiple-classifier systems (MCS). While some of the investigated optimisation problems naturally lend themselves to the type of optimisation for which PSO is most suitable we present some other applications requiring non-standard representation of the particles as well as handling of constraints in the optimisation process. In the most typical optimisation case the continuous version of PSO has been successfully applied for the optimization of a soft-linear combiner. On the other hand, one of the adapted binary versions of PSO has been shown to work well in the case of multi-stage organization of majority voting (MOMV), where the search dimension is high and the local search techniques can often get stuck in local optima. All three presented PSO based methods have been tested and compared to each other and to forward search and stochastic hillclimber for five real-world non-trivial datasets.
Abstract In many industry and research areas, data mining is a crucial process. This paper presen... more Abstract In many industry and research areas, data mining is a crucial process. This paper presents an evolving structure of classifiers (random forest) where the trees are generated by hybrid method combining ant colony metaheuristics and evolutionary computing ...
Abstract In many industry and research areas, data mining is a crucial process. This paper presen... more Abstract In many industry and research areas, data mining is a crucial process. This paper presents an evolving structure of classifiers (random forest) where the trees are generated by hybrid method combining ant colony metaheuristics and evolutionary computing ...
The paper proposes a method for the classification of EEG signal based on machine learning method... more The paper proposes a method for the classification of EEG signal based on machine learning methods. We analyzed the data from an EEG experiment consisting of affective picture stimuli presentation, and tested automatic recognition of the individual emotional states from the EEG signal using Bayes classifier. The mean accuracy was about 75 percent, but we were not able to select universal features for classification of all subjects, because of interindividual differences in the signal. We also identified correlation between the classification error and the extroversion-introversion personality trait measured by EPQ-R test. Introverts have lower excitation threshold so we are able to detect the differences in their EEG activity with better accuracy. Furthermore, the use of Kohonen's self-organizing map for visualization is suggested and demonstrated on one subject.
International Journal of Computational Intelligence Research, 2008
This paper proposes some ideas and suggestions describing application of dynamic theory of social... more This paper proposes some ideas and suggestions describing application of dynamic theory of social impact (SI) simulations for optimization. This connection could lead to development of a novel psychologically inspired optimization scheme. The main aim is to use some macro level phenomena emerged from micro level SI theory in optimization. This aim is reached by introduction of fitness evaluation to SI simulation framework. For demonstration of optimization abilities of SI simulation, some preliminary experiments are presented.
The human opinion formation can be understood as a social approach to optimization. In the real w... more The human opinion formation can be understood as a social approach to optimization. In the real world, the opinions encode a candidate solution, which is evaluated by a complex and unknown fitness function. The computer models of such processes can be slightly modified by introducing a fitness value, which leads to novel family of optimization techniques. This paper demonstrates how the novel algorithms can be derived from opinion formation models and empirically proves their usability in the area of binary optimization. Particularly, it introduces a general SITO algorithmic framework and describes three algorithms based on this general framework - the previously proposed original distance-based (oSITO), the simplified (sSITO) and the Galam inspired (gSITO) algorithm.
The Social Impact Theory based Optimizer (SITO) is a novel population-based binary optimization m... more The Social Impact Theory based Optimizer (SITO) is a novel population-based binary optimization metaheuristic, inspired by computer simulation of Dynamic Theory of Social Impact. Its first version has been developed in 2006. Recently, we can summarize some of most important aspects of SITO, including some ideas for future work.
In nature, industry, medicine, social environment, simply everywhere we find a lot of data that b... more In nature, industry, medicine, social environment, simply everywhere we find a lot of data that bear certain information. A dictionary defines data as facts or figures from which conclusions may be drawn. Data can be classified as either numeric or nonnumeric. The structure and nature of data greatly affects the choice of analysis method. Under the term structure we understand the facts that the data might be not a single number but n-tuples of measurements. Structure is also very closely linked to the reason of data collection and method of measurement. The discussion is focused on the similarities and differences of nature inspired methods and their natural counterparts in light of continuous and discrete properties. Different examples of nature inspired methods will be inspected in terms of data, problem domains and inner structure and principles.
A robust, automated classification system for polysomnographic (PSG) data targeted to the newborn... more A robust, automated classification system for polysomnographic (PSG) data targeted to the newborn sleep stage identification is presented. The problem of polysomnographic signal classification is very often difficult because of artifacts and noise. Furthermore, for each signal, a special classification method for each particular type of segment must be mostly used. This paper proposes fully unsupervised approach using adaptive segmentation, appropriate features extraction and hierarchical clustering (Ward’s minimumvariance method is used). The mutual information concept was applied to results of hierarchical clustering. The proposed procedure was tested on real neonatal data. All sleep states were successfully separated by a combination of EEG, EMG, EOG, PNG and ECG channels.
This report describes the solution of NiSIS Competition 2006 awarded by "best nature inspired con... more This report describes the solution of NiSIS Competition 2006 awarded by "best nature inspired concept" award.
The Social Impact Theory based Optimizer (SITO) is a novel population-based binary optimization m... more The Social Impact Theory based Optimizer (SITO) is a novel population-based binary optimization metaheuristic, inspired by computer simulation of Dynamic Theory of Social Impact. Its first version has been developed in 2006. Recently, we can summarize some of most important aspects of SITO, including some ideas for future work.
In nature, industry, medicine, social environment, simply everywhere we find a lot of data that b... more In nature, industry, medicine, social environment, simply everywhere we find a lot of data that bear certain information. A dictionary defines data as facts or figures from which conclusions may be drawn. Data can be classified as either numeric or nonnumeric. The structure and nature of data greatly affects the choice of analysis method. Under the term structure we understand the facts that the data might be not a single number but n-tuples of measurements. Structure is also very closely linked to the reason of data collection and method of measurement. The discussion is focused on the similarities and differences of nature inspired methods and their natural counterparts in light of continuous and discrete properties. Different examples of nature inspired methods will be inspected in terms of data, problem domains and inner structure and principles.
A robust, automated classification system for polysomnographic (PSG) data targeted to the newborn... more A robust, automated classification system for polysomnographic (PSG) data targeted to the newborn sleep stage identification is presented. The problem of polysomnographic signal classification is very often difficult because of artifacts and noise. Furthermore, for each signal, a special classification method for each particular type of segment must be mostly used. This paper proposes fully unsupervised approach using adaptive segmentation, appropriate features extraction and hierarchical clustering (Ward’s minimumvariance method is used). The mutual information concept was applied to results of hierarchical clustering. The proposed procedure was tested on real neonatal data. All sleep states were successfully separated by a combination of EEG, EMG, EOG, PNG and ECG channels.
Robot path planning problem is one of most important task mobile robots. This paper proposes an o... more Robot path planning problem is one of most important task mobile robots. This paper proposes an original approach using a path description by string of cubic splines. Such path is easy executable and natural for car-like robot. Furthermore, it is possible to ensure smooth derivation in connections of particular splines. In this case, the path planning is equivalent to optimization of parameters of splines. An evolutionary technique called particle swarm optimization (PSO) was used hereunder due to its relatively fast convergence and global search character. Various settings of PSO parameters were tested and the best setting was compared to two classical mobile robot path planning algorithms.
The main goal of the study is an unsupervised classification of school children dyslexia. Eye mov... more The main goal of the study is an unsupervised classification of school children dyslexia. Eye movements of 49 female subjects were measured using videooculographic technique (VOG) during two non-reading and one reading tasks. A feature selection was performed obtaining data set consisting of 26 features. Next an inductive modelling technique was applied to data set resulting in extraction of six features which were used as the input to self-organizing map (SOM). Three clusters were finally formed by the SOM proving that the proposed methodology is suitable for automatic dyslexia analysis.
Biological processes and methods have been influencing science and technology for many decades. T... more Biological processes and methods have been influencing science and technology for many decades. The ideas of feedback and control processes Norbert Wiener used in his cybernetics were based on observation of these phenomena in biological systems. Artificial intelligence and intelligent systems have been fundamentally interested in the phenomenology of living systems, namely perception, decision-making, action, and learning. Natural systems exhibit many properties that form fundamentals for a number of nature inspired applications -dynamics, flexibility, robustness, self-organisation, simplicity of basic elements, and decentralization. This paper reviews examples of nature inspired software applications, mostly drawing inspiration from collective behaviour of social colonies.
This paper presents new application of Particle Swarm Optimization (PSO) algorithm for training H... more This paper presents new application of Particle Swarm Optimization (PSO) algorithm for training Hidden Markov Models (HMMs). The problem of finding an optimal set of model parameters is numerical optimization problem constrained by stochastic character of HMM parameters. Constraint handling is carried out using three different ways and the results are compared to Baum-Welch algorithm (BW), commonly used for HMM training. The global searching PSO method is much less sensitive to local extremes and finds better solutions than the local BW algorithm, which often converges to local optima. The advantage of PSO approach was markedly evident, when longer training sequence was used.
Biological processes and methods have been influencing science and technology for many decades. T... more Biological processes and methods have been influencing science and technology for many decades. The ideas of feedback and control processes Norbert Wiener used in his cybernetics were based on observation of these phenomena in biological systems. Artificial intelligence and intelligent systems have been fundamentally interested in the phenomenology of living systems, namely perception, decision-making, action, and learning. Natural systems exhibit many properties that form fundamentals for a number of nature inspired applications – dynamics, flexibility, robustness, self-organisation, simplicity of basic elements, and decentralization. This paper reviews examples of nature inspired software applications focused on optimization problems, mostly drawing inspiration from collective behaviour of social colonies.
This paper introduces a novel stochastic and population-based binary optimization method inspired... more This paper introduces a novel stochastic and population-based binary optimization method inspired by social psychology. It is called Social Impact Theory based Optimization (SITO). The method has been developed with the use of some simple modifications of simulations of Latané’s Dynamic Social Impact Theory. The usability of the algorithm is demonstrated via experimental testing on some test problems. The results showed that the initial version of SITO performs comparably to the simple Genetic Algorithm (GA) and the binary Particle Swarm Optimization (bPSO).
The interactions taking place in the society could be a source of rich inspiration for the develo... more The interactions taking place in the society could be a source of rich inspiration for the development of novel computational methods. This paper describes an application of two optimization methods based on the idea of social interactions. The first one is the Social Impact Theory based Optimizer - a novel method directly inspired by and based on the Dynamic Theory of Social Impact known from social psychology. The second one is the binary Particle Swarm Optimization - well known optimization technique, which could be understood as to be inspired by decision making process in a group. The two binary optimization methods are applied in the area of automatic pattern classification to selection of an optimal subset of classifier’s inputs. The testing is performed using four datasets from UCI repository. The results show the ability of both methods to significantly reduce input dimensionality and simultaneously keep up the generalization ability.
In this paper we present application of various versions of the particle swarm optimization metho... more In this paper we present application of various versions of the particle swarm optimization method (PSO) in the process of generation of multiple-classifier systems (MCS). While some of the investigated optimisation problems naturally lend themselves to the type of optimisation for which PSO is most suitable we present some other applications requiring non-standard representation of the particles as well as handling of constraints in the optimisation process. In the most typical optimisation case the continuous version of PSO has been successfully applied for the optimization of a soft-linear combiner. On the other hand, one of the adapted binary versions of PSO has been shown to work well in the case of multi-stage organization of majority voting (MOMV), where the search dimension is high and the local search techniques can often get stuck in local optima. All three presented PSO based methods have been tested and compared to each other and to forward search and stochastic hillclimber for five real-world non-trivial datasets.
Abstract In many industry and research areas, data mining is a crucial process. This paper presen... more Abstract In many industry and research areas, data mining is a crucial process. This paper presents an evolving structure of classifiers (random forest) where the trees are generated by hybrid method combining ant colony metaheuristics and evolutionary computing ...
Abstract In many industry and research areas, data mining is a crucial process. This paper presen... more Abstract In many industry and research areas, data mining is a crucial process. This paper presents an evolving structure of classifiers (random forest) where the trees are generated by hybrid method combining ant colony metaheuristics and evolutionary computing ...
The paper proposes a method for the classification of EEG signal based on machine learning method... more The paper proposes a method for the classification of EEG signal based on machine learning methods. We analyzed the data from an EEG experiment consisting of affective picture stimuli presentation, and tested automatic recognition of the individual emotional states from the EEG signal using Bayes classifier. The mean accuracy was about 75 percent, but we were not able to select universal features for classification of all subjects, because of interindividual differences in the signal. We also identified correlation between the classification error and the extroversion-introversion personality trait measured by EPQ-R test. Introverts have lower excitation threshold so we are able to detect the differences in their EEG activity with better accuracy. Furthermore, the use of Kohonen's self-organizing map for visualization is suggested and demonstrated on one subject.
International Journal of Computational Intelligence Research, 2008
This paper proposes some ideas and suggestions describing application of dynamic theory of social... more This paper proposes some ideas and suggestions describing application of dynamic theory of social impact (SI) simulations for optimization. This connection could lead to development of a novel psychologically inspired optimization scheme. The main aim is to use some macro level phenomena emerged from micro level SI theory in optimization. This aim is reached by introduction of fitness evaluation to SI simulation framework. For demonstration of optimization abilities of SI simulation, some preliminary experiments are presented.
The human opinion formation can be understood as a social approach to optimization. In the real w... more The human opinion formation can be understood as a social approach to optimization. In the real world, the opinions encode a candidate solution, which is evaluated by a complex and unknown fitness function. The computer models of such processes can be slightly modified by introducing a fitness value, which leads to novel family of optimization techniques. This paper demonstrates how the novel algorithms can be derived from opinion formation models and empirically proves their usability in the area of binary optimization. Particularly, it introduces a general SITO algorithmic framework and describes three algorithms based on this general framework - the previously proposed original distance-based (oSITO), the simplified (sSITO) and the Galam inspired (gSITO) algorithm.
The Social Impact Theory based Optimizer (SITO) is a novel population-based binary optimization m... more The Social Impact Theory based Optimizer (SITO) is a novel population-based binary optimization metaheuristic, inspired by computer simulation of Dynamic Theory of Social Impact. Its first version has been developed in 2006. Recently, we can summarize some of most important aspects of SITO, including some ideas for future work.
In nature, industry, medicine, social environment, simply everywhere we find a lot of data that b... more In nature, industry, medicine, social environment, simply everywhere we find a lot of data that bear certain information. A dictionary defines data as facts or figures from which conclusions may be drawn. Data can be classified as either numeric or nonnumeric. The structure and nature of data greatly affects the choice of analysis method. Under the term structure we understand the facts that the data might be not a single number but n-tuples of measurements. Structure is also very closely linked to the reason of data collection and method of measurement. The discussion is focused on the similarities and differences of nature inspired methods and their natural counterparts in light of continuous and discrete properties. Different examples of nature inspired methods will be inspected in terms of data, problem domains and inner structure and principles.
A robust, automated classification system for polysomnographic (PSG) data targeted to the newborn... more A robust, automated classification system for polysomnographic (PSG) data targeted to the newborn sleep stage identification is presented. The problem of polysomnographic signal classification is very often difficult because of artifacts and noise. Furthermore, for each signal, a special classification method for each particular type of segment must be mostly used. This paper proposes fully unsupervised approach using adaptive segmentation, appropriate features extraction and hierarchical clustering (Ward’s minimumvariance method is used). The mutual information concept was applied to results of hierarchical clustering. The proposed procedure was tested on real neonatal data. All sleep states were successfully separated by a combination of EEG, EMG, EOG, PNG and ECG channels.
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