Pub Date : 1900-01-01DOI: 10.4018/978-1-60566-705-8.ch004
Eugene Ch’ng
The complexity of nature can only be solved by nature’s intrinsic problem-solving approach. Therefore, the computational modelling of nature requires careful observations of its underlying principles in order that these laws can be abstracted into formulas suitable for the algorithmic configuration. This chapter proposes a novel modelling approach for biodiversity informatics research. The approach is based on the emergence phenomenon for predicting vegetation distribution patterns in a multi-variable ecosystem where Artificial Life-based vegetation grow, compete, adapt, reproduce and conquer plots of landscape in order to survive their generation. The feasibility of the modelling approach presented in this chapter may provide a firm foundation not only for predicting vegetation distribution in a wide variety of landscapes, but could also be extended for studying biodiversity and the loss of animal species for sustainable management of resources.
{"title":"An Artificial Life-Based Vegetation Modelling Approach for Biodiversity Research","authors":"Eugene Ch’ng","doi":"10.4018/978-1-60566-705-8.ch004","DOIUrl":"https://doi.org/10.4018/978-1-60566-705-8.ch004","url":null,"abstract":"The complexity of nature can only be solved by nature’s intrinsic problem-solving approach. Therefore, the computational modelling of nature requires careful observations of its underlying principles in order that these laws can be abstracted into formulas suitable for the algorithmic configuration. This chapter proposes a novel modelling approach for biodiversity informatics research. The approach is based on the emergence phenomenon for predicting vegetation distribution patterns in a multi-variable ecosystem where Artificial Life-based vegetation grow, compete, adapt, reproduce and conquer plots of landscape in order to survive their generation. The feasibility of the modelling approach presented in this chapter may provide a firm foundation not only for predicting vegetation distribution in a wide variety of landscapes, but could also be extended for studying biodiversity and the loss of animal species for sustainable management of resources.","PeriodicalId":222582,"journal":{"name":"Nature-Inspired Informatics for Intelligent Applications and Knowledge Discovery","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124059860","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 : 1900-01-01DOI: 10.4018/978-1-60566-705-8.ch007
C. Greene, J. Moore
In human genetics the availability of chip-based technology facilitates the measurement of thousands of DNA sequence variations from across the human genome. The informatics challenge is to identify combinations of interacting DNA sequence variations that predict common diseases. The authors review three nature-inspired methods that have been developed and evaluated in this domain. The two approaches this chapter focuses on in detail are genetic programming (GP) and a complex-system inspired GPlike computational evolution system (CES). The authors also discuss a third nature-inspired approach known as ant colony optimization (ACO). The GP and ACO techniques are designed to select relevant attributes, while the CES addresses both the selection of relevant attributes and the modeling of disease risk. Specifically, they examine these methods in the context of epistasis or gene-gene interactions. For the work discussed here we focus solely on the situation where there is an epistatic effect but no detectable main effect. In this domain, early studies show that nature-inspired algorithms perform no better than a simple random search when classification accuracy is used as the fitness function. Thus, the challenge for applying these search algorithms to this problem is that when using classification accuracy there are no building blocks. The goal then is to use outside knowledge or pre-processing of the dataset to provide these building blocks in a manner that enables the population, in a nature-inspired framework,
{"title":"Solving Complex Problems in Human Genetics using Nature-Inspired Algorithms Requires Strategies which Exploit Domain-Specific Knowledge","authors":"C. Greene, J. Moore","doi":"10.4018/978-1-60566-705-8.ch007","DOIUrl":"https://doi.org/10.4018/978-1-60566-705-8.ch007","url":null,"abstract":"In human genetics the availability of chip-based technology facilitates the measurement of thousands of DNA sequence variations from across the human genome. The informatics challenge is to identify combinations of interacting DNA sequence variations that predict common diseases. The authors review three nature-inspired methods that have been developed and evaluated in this domain. The two approaches this chapter focuses on in detail are genetic programming (GP) and a complex-system inspired GPlike computational evolution system (CES). The authors also discuss a third nature-inspired approach known as ant colony optimization (ACO). The GP and ACO techniques are designed to select relevant attributes, while the CES addresses both the selection of relevant attributes and the modeling of disease risk. Specifically, they examine these methods in the context of epistasis or gene-gene interactions. For the work discussed here we focus solely on the situation where there is an epistatic effect but no detectable main effect. In this domain, early studies show that nature-inspired algorithms perform no better than a simple random search when classification accuracy is used as the fitness function. Thus, the challenge for applying these search algorithms to this problem is that when using classification accuracy there are no building blocks. The goal then is to use outside knowledge or pre-processing of the dataset to provide these building blocks in a manner that enables the population, in a nature-inspired framework,","PeriodicalId":222582,"journal":{"name":"Nature-Inspired Informatics for Intelligent Applications and Knowledge Discovery","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127162701","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 : 1900-01-01DOI: 10.4018/978-1-60566-705-8.ch006
R. Logeswaran
Automatic detection of tumours in the bile ducts of the liver is very difficult as often, in the de-facto non-invasive diagnostic images using magnetic resonance cholangiopancreatography (MRCP), tumours are not clearly visible. Specialists use their experience in anatomy to diagnose a tumour by absence of expected structures in the images. Naturally, undertaking such diagnosis is very difficult for an automated system. This chapter proposes an algorithm that is based on a combination of the manual diagnosis principles along with nature-inspired image processing techniques and artificial neural networks (ANN) to assist in the preliminary diagnosis of tumours affecting the bile ducts in the liver. The results obtained show over 88% success rate of the system developed using an ANN with the multi-layer perceptron (MLP) architecture, in performing the difficult automated preliminary detection of the tumours, even in the robust clinical test images with other biliary diseases present.
{"title":"Neural Networks in Medicine","authors":"R. Logeswaran","doi":"10.4018/978-1-60566-705-8.ch006","DOIUrl":"https://doi.org/10.4018/978-1-60566-705-8.ch006","url":null,"abstract":"Automatic detection of tumours in the bile ducts of the liver is very difficult as often, in the de-facto non-invasive diagnostic images using magnetic resonance cholangiopancreatography (MRCP), tumours are not clearly visible. Specialists use their experience in anatomy to diagnose a tumour by absence of expected structures in the images. Naturally, undertaking such diagnosis is very difficult for an automated system. This chapter proposes an algorithm that is based on a combination of the manual diagnosis principles along with nature-inspired image processing techniques and artificial neural networks (ANN) to assist in the preliminary diagnosis of tumours affecting the bile ducts in the liver. The results obtained show over 88% success rate of the system developed using an ANN with the multi-layer perceptron (MLP) architecture, in performing the difficult automated preliminary detection of the tumours, even in the robust clinical test images with other biliary diseases present.","PeriodicalId":222582,"journal":{"name":"Nature-Inspired Informatics for Intelligent Applications and Knowledge Discovery","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121471643","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 : 1900-01-01DOI: 10.4018/978-1-60566-705-8.ch003
Markus Kress, Sanaz Mostaghim, D. Seese
In this chapter, the authors study a new variant of Particle Swarm Optimization (PSO) to efficiently execute business processes. The main challenge of this application for the PSO is that the function evaluations typically take a high computation time. They propose the Gap Search (GS) method in combination with the PSO to perform a better exploration in the search space and study its influence on the results of our application. They replace the random initialization of the solutions for the initial population as well as for the diversity preservation method with the GS method. The experimental results show that the GS method significantly improves the quality of the solutions and obtains better results for the application as compared to the results of a standard PSO and Genetic Algorithms. Moreover, the combination of the methods the authors used show promising results as tools to be applied for improvement of Business Process Optimization.
{"title":"Intelligent Business Process Execution using Particle Swarm Optimization","authors":"Markus Kress, Sanaz Mostaghim, D. Seese","doi":"10.4018/978-1-60566-705-8.ch003","DOIUrl":"https://doi.org/10.4018/978-1-60566-705-8.ch003","url":null,"abstract":"In this chapter, the authors study a new variant of Particle Swarm Optimization (PSO) to efficiently execute business processes. The main challenge of this application for the PSO is that the function evaluations typically take a high computation time. They propose the Gap Search (GS) method in combination with the PSO to perform a better exploration in the search space and study its influence on the results of our application. They replace the random initialization of the solutions for the initial population as well as for the diversity preservation method with the GS method. The experimental results show that the GS method significantly improves the quality of the solutions and obtains better results for the application as compared to the results of a standard PSO and Genetic Algorithms. Moreover, the combination of the methods the authors used show promising results as tools to be applied for improvement of Business Process Optimization.","PeriodicalId":222582,"journal":{"name":"Nature-Inspired Informatics for Intelligent Applications and Knowledge Discovery","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133730415","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 : 1900-01-01DOI: 10.4018/978-1-60566-705-8.ch002
Muneer Buckley, Z. Michalewicz, R. Zurbruegg
There is a great need for accurate predictions of foreign exchange rates. Many industries participate in foreign exchange scenarios with little idea where the exchange rate is moving, and what the optimum decision to make at any given time is. Although current economic models do exist for this purpose, improvements could be made in both their flexibility and adaptability. This provides much room for models that do not suffer from such constraints. This chapter proposes the use of a genetic program (GP) to predict future foreign exchange rates. The GP is an extension of the DyFor GP tailored for forecasting in dynamic environments. The GP is tested on the Australian / US (AUD/USD) exchange rate and compared against a basic economic model. The results show that the system has potential in forecasting long term values, and may do so better than established models. Further improvements are also suggested. DOI: 10.4018/978-1-60566-705-8.ch002
{"title":"An Application of Genetic Programming to Forecasting Foreign Exchange Rates","authors":"Muneer Buckley, Z. Michalewicz, R. Zurbruegg","doi":"10.4018/978-1-60566-705-8.ch002","DOIUrl":"https://doi.org/10.4018/978-1-60566-705-8.ch002","url":null,"abstract":"There is a great need for accurate predictions of foreign exchange rates. Many industries participate in foreign exchange scenarios with little idea where the exchange rate is moving, and what the optimum decision to make at any given time is. Although current economic models do exist for this purpose, improvements could be made in both their flexibility and adaptability. This provides much room for models that do not suffer from such constraints. This chapter proposes the use of a genetic program (GP) to predict future foreign exchange rates. The GP is an extension of the DyFor GP tailored for forecasting in dynamic environments. The GP is tested on the Australian / US (AUD/USD) exchange rate and compared against a basic economic model. The results show that the system has potential in forecasting long term values, and may do so better than established models. Further improvements are also suggested. DOI: 10.4018/978-1-60566-705-8.ch002","PeriodicalId":222582,"journal":{"name":"Nature-Inspired Informatics for Intelligent Applications and Knowledge Discovery","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133479442","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 : 1900-01-01DOI: 10.4018/978-1-60566-705-8.ch013
Moussa Diaf, K. Hammouche, P. Siarry
abstract Biological studies highlighting the collective behavior of ants in fulfilling various tasks by using their complex indirect communication process have constituted the starting point for many physical systems and various ant colony algorithms. Each ant colony is considered as a superorganism which operates as a unified entity made up of simple agents. These agents (ants) interact locally with one another and with their environment, particularly in finding the shortest path from the nest to food sources without any centralized control dictating the behavior of individual agents. It is this coordination mechanism that has inspired researchers to develop plenty of metaheuristic algorithms in order to find good solutions for NP-hard combinatorial optimization problems. In this chapter, the authors give a biological description of these fascinating insects and their complex indirect communication process. From this rich source of inspiration for researchers, the authors show how, through the real ant, artificial ant is modeled and applied in combinatorial optimization, data clustering, collective robotics, and image processing.
{"title":"From the Real Ant to the Artificial Ant","authors":"Moussa Diaf, K. Hammouche, P. Siarry","doi":"10.4018/978-1-60566-705-8.ch013","DOIUrl":"https://doi.org/10.4018/978-1-60566-705-8.ch013","url":null,"abstract":"abstract Biological studies highlighting the collective behavior of ants in fulfilling various tasks by using their complex indirect communication process have constituted the starting point for many physical systems and various ant colony algorithms. Each ant colony is considered as a superorganism which operates as a unified entity made up of simple agents. These agents (ants) interact locally with one another and with their environment, particularly in finding the shortest path from the nest to food sources without any centralized control dictating the behavior of individual agents. It is this coordination mechanism that has inspired researchers to develop plenty of metaheuristic algorithms in order to find good solutions for NP-hard combinatorial optimization problems. In this chapter, the authors give a biological description of these fascinating insects and their complex indirect communication process. From this rich source of inspiration for researchers, the authors show how, through the real ant, artificial ant is modeled and applied in combinatorial optimization, data clustering, collective robotics, and image processing.","PeriodicalId":222582,"journal":{"name":"Nature-Inspired Informatics for Intelligent Applications and Knowledge Discovery","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129183276","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 : 1900-01-01DOI: 10.4018/978-1-60566-705-8.ch012
S. V. Peña, A. H. Aguirre, S. Rionda, C. A. Delgado
The authors introduce new approaches for the combinational circuit design based on Estimation of Distribution Algorithms. In this paradigm, the structure and data dependencies embedded in the data (population of candidate circuits) are modeled by a conditional probability distribution function. The new population is simulated from the probability model thus inheriting the dependencies. The authors explain the procedure to build an approximation of the probability distribution through two approaches: polytrees and Bayesian networks. A set of circuit design experiments is performed and a comparison with evolutionary approaches is reported.
{"title":"Combinational Circuit Design with Estimation of Distribution Algorithms","authors":"S. V. Peña, A. H. Aguirre, S. Rionda, C. A. Delgado","doi":"10.4018/978-1-60566-705-8.ch012","DOIUrl":"https://doi.org/10.4018/978-1-60566-705-8.ch012","url":null,"abstract":"The authors introduce new approaches for the combinational circuit design based on Estimation of Distribution Algorithms. In this paradigm, the structure and data dependencies embedded in the data (population of candidate circuits) are modeled by a conditional probability distribution function. The new population is simulated from the probability model thus inheriting the dependencies. The authors explain the procedure to build an approximation of the probability distribution through two approaches: polytrees and Bayesian networks. A set of circuit design experiments is performed and a comparison with evolutionary approaches is reported.","PeriodicalId":222582,"journal":{"name":"Nature-Inspired Informatics for Intelligent Applications and Knowledge Discovery","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126777480","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 : 1900-01-01DOI: 10.4018/978-1-60566-705-8.ch014
Sergio Nesmachnow, H. Cancela, E. Alba
The speedy pace of change in telecommunications and its ubiquitous presence have drastically altered the way people interact, impacting production, government, and social life. The infrastructure for providing telecommunication services must be continuously renewed, as innovative technologies emerge and drive changes by offering to bring new services to the end users. In this context, the problem of efficiently designing the underlying networks in order to satisfy different requirements while at the same time keeping the capital and operative expenditures bounded is of ever growing importance and actuality. Network design problems have many variations, depending on the characteristics of the technologies to be employed, as well as on the simplifying hypothesis that can be applied on each particular context, and on the planning horizon. Nevertheless, in most cases they are extremely complex problems, for which exact solutions can not be found in practice. Nature-inspired optimization techniques (belonging to the metaheuristic family of computational methods) are important tools in these cases, as their application allows obtaining good quality solutions in reasonable computational times. The objective of this work is to present a systematic review of the application of natureinspired techniques to solve optimization problems related to telecommunication network design. The review is aimed at providing an insight of different approaches in the area, in particular covering four main classes of applications: minimum spanning trees, reliable networks, local access network design and backbone location, and cellular and wireless network design. A large proportion of the papers deal with single objective models, but a growing number of works study multi-objective problems, where it is necessary to find solutions which perform well in a number of different criteria. While genetic algorithms and other evolutionary algorithms appear most frequently, there is also significant research on the application of other methods, such as ant colony optimization, particle swarm optimization, immune systems, and other nature-inspired agent-based techniques.
{"title":"Nature-Inspired Informatics for Telecommunication Network Design","authors":"Sergio Nesmachnow, H. Cancela, E. Alba","doi":"10.4018/978-1-60566-705-8.ch014","DOIUrl":"https://doi.org/10.4018/978-1-60566-705-8.ch014","url":null,"abstract":"The speedy pace of change in telecommunications and its ubiquitous presence have drastically altered the way people interact, impacting production, government, and social life. The infrastructure for providing telecommunication services must be continuously renewed, as innovative technologies emerge and drive changes by offering to bring new services to the end users. In this context, the problem of efficiently designing the underlying networks in order to satisfy different requirements while at the same time keeping the capital and operative expenditures bounded is of ever growing importance and actuality. Network design problems have many variations, depending on the characteristics of the technologies to be employed, as well as on the simplifying hypothesis that can be applied on each particular context, and on the planning horizon. Nevertheless, in most cases they are extremely complex problems, for which exact solutions can not be found in practice. Nature-inspired optimization techniques (belonging to the metaheuristic family of computational methods) are important tools in these cases, as their application allows obtaining good quality solutions in reasonable computational times. The objective of this work is to present a systematic review of the application of natureinspired techniques to solve optimization problems related to telecommunication network design. The review is aimed at providing an insight of different approaches in the area, in particular covering four main classes of applications: minimum spanning trees, reliable networks, local access network design and backbone location, and cellular and wireless network design. A large proportion of the papers deal with single objective models, but a growing number of works study multi-objective problems, where it is necessary to find solutions which perform well in a number of different criteria. While genetic algorithms and other evolutionary algorithms appear most frequently, there is also significant research on the application of other methods, such as ant colony optimization, particle swarm optimization, immune systems, and other nature-inspired agent-based techniques.","PeriodicalId":222582,"journal":{"name":"Nature-Inspired Informatics for Intelligent Applications and Knowledge Discovery","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116660493","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 : 1900-01-01DOI: 10.4018/978-1-60566-705-8.ch009
Erwan Le Martelot, P. Bentley
{"title":"Modelling Biological Processes Naturally using Systemic Computation","authors":"Erwan Le Martelot, P. Bentley","doi":"10.4018/978-1-60566-705-8.ch009","DOIUrl":"https://doi.org/10.4018/978-1-60566-705-8.ch009","url":null,"abstract":"","PeriodicalId":222582,"journal":{"name":"Nature-Inspired Informatics for Intelligent Applications and Knowledge Discovery","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126082978","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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