Pub Date : 2013-06-20DOI: 10.1109/CEC.2013.6557775
R. R. Oliveira, T. Heimfarth, R. W. Bettio, M. Arantes, C. Toledo
The development of Wireless Sensor Networks (WSNs) applications is an arduous task, since the application needs to be customized for each sensor. Thus, the automatic generation of WSN's applications is desirable to reduce costs, since it drastically reduces the human effort. This paper presents the use of Genetic Programming to automatically generate WSNs applications. A scripting language based on events and actions is proposed to represent the WSN behavior. Events represent the state of a given sensor node and actions modify these states. Some events are internal states and others are external states captured by the sensors. The genetic programming is used to automatically generate WSNs applications described using this scripting language. These scripts are executed by all network's sensors. This approach enables the application designer to define only the overall objective of the WSN. This objective is defined by means of a fitness function. An event-detection problem is presented in order to evaluate the proposed method. The results shown the capability of the developed approach to successfully solve WSNs problems through the automatic generation of applications.
{"title":"A Genetic Programming based approach to automatically generate Wireless Sensor Networks applications","authors":"R. R. Oliveira, T. Heimfarth, R. W. Bettio, M. Arantes, C. Toledo","doi":"10.1109/CEC.2013.6557775","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557775","url":null,"abstract":"The development of Wireless Sensor Networks (WSNs) applications is an arduous task, since the application needs to be customized for each sensor. Thus, the automatic generation of WSN's applications is desirable to reduce costs, since it drastically reduces the human effort. This paper presents the use of Genetic Programming to automatically generate WSNs applications. A scripting language based on events and actions is proposed to represent the WSN behavior. Events represent the state of a given sensor node and actions modify these states. Some events are internal states and others are external states captured by the sensors. The genetic programming is used to automatically generate WSNs applications described using this scripting language. These scripts are executed by all network's sensors. This approach enables the application designer to define only the overall objective of the WSN. This objective is defined by means of a fitness function. An event-detection problem is presented in order to evaluate the proposed method. The results shown the capability of the developed approach to successfully solve WSNs problems through the automatic generation of applications.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131797121","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 : 2013-06-20DOI: 10.1109/CEC.2013.6557718
A. Valsecchi, Jérémie Dubois-Lacoste, T. Stützle, S. Damas, J. Santamaría, L. Marrakchi-Kacem
Image registration is a fundamental step in combining information from multiple images in medical imaging, computer vision and image processing. In this paper, we configure a recent evolutionary algorithm for medical image registration, r-GA, with an offline automatic parameter tuning technique. In addition, we demonstrate the use of automatic tuning to compare different registration algorithms, since it allows to consider results that are not affected by the ability and efforts invested by the designers in configuring the different algorithms, a crucial task that strongly impacts their performance. Our experimental study is carried out on a large dataset of brain MRI, on which we compare the performance of r-GA with four classic IR techniques. Our results show that all algorithms benefit from the automatic tuning process and indicate that r-GA performs significantly better than the competitors.
{"title":"Evolutionary medical image registration using automatic parameter tuning","authors":"A. Valsecchi, Jérémie Dubois-Lacoste, T. Stützle, S. Damas, J. Santamaría, L. Marrakchi-Kacem","doi":"10.1109/CEC.2013.6557718","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557718","url":null,"abstract":"Image registration is a fundamental step in combining information from multiple images in medical imaging, computer vision and image processing. In this paper, we configure a recent evolutionary algorithm for medical image registration, r-GA, with an offline automatic parameter tuning technique. In addition, we demonstrate the use of automatic tuning to compare different registration algorithms, since it allows to consider results that are not affected by the ability and efforts invested by the designers in configuring the different algorithms, a crucial task that strongly impacts their performance. Our experimental study is carried out on a large dataset of brain MRI, on which we compare the performance of r-GA with four classic IR techniques. Our results show that all algorithms benefit from the automatic tuning process and indicate that r-GA performs significantly better than the competitors.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132122423","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 : 2013-06-20DOI: 10.1109/CEC.2013.6557649
Antonino Feitosa Neto, A. Canuto, Teresa B Ludermir
This paper investigates the influence of measures of good and bad diversity when used explicitly to guide the search of a genetic algorithm to design ensemble systems. We then analyze what the best set of objectives between classification error, good diversity and bad diversity as well as all combination of them. In this analysis, we make use of the NSGA II algorithm in order to generate ensemble systems, using k-NN as individual classifiers and majority vote as the combination method. The main goal of this investigation is to determine which set of objectives generates more accurate ensembles. In addition, we aim to analyze whether or not the diversity measures (good and bad diversity) have a positive effect in the construction of ensembles and if they can replace the classification error as optimization objective without causing losses in the accuracy level of the generated ensembles.
{"title":"Using good and bad diversity measures in the design of ensemble systems: A genetic algorithm approach","authors":"Antonino Feitosa Neto, A. Canuto, Teresa B Ludermir","doi":"10.1109/CEC.2013.6557649","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557649","url":null,"abstract":"This paper investigates the influence of measures of good and bad diversity when used explicitly to guide the search of a genetic algorithm to design ensemble systems. We then analyze what the best set of objectives between classification error, good diversity and bad diversity as well as all combination of them. In this analysis, we make use of the NSGA II algorithm in order to generate ensemble systems, using k-NN as individual classifiers and majority vote as the combination method. The main goal of this investigation is to determine which set of objectives generates more accurate ensembles. In addition, we aim to analyze whether or not the diversity measures (good and bad diversity) have a positive effect in the construction of ensembles and if they can replace the classification error as optimization objective without causing losses in the accuracy level of the generated ensembles.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134334350","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 : 2013-06-20DOI: 10.1109/CEC.2013.6557930
R. Prati, F. O. França
In some data mining applications the analyzed data can be classified as simultaneously belonging to more than one class, this characterizes the multi-label classification problem. Numerous methods for dealing with this problem are based on decomposition, which essentially treats labels (or some subsets of labels) independently and ignores interactions between them. This fact might be a problem, as some labels may be correlated to local patterns in the data. In this paper, we propose to enhance multi-label classifiers with the aid of biclusters, which are capable of finding the correlation between subsets of objects, features and labels. We then construct binary features from these patterns that can be interpreted as local correlations (in terms of subset of features and instances) in the data. These features are used as input for multi-label classifiers. We experimentally show that using such constructed features can improve the classification performance of some decompositive multi-label learning techniques.
{"title":"Extending features for multilabel classification with swarm biclustering","authors":"R. Prati, F. O. França","doi":"10.1109/CEC.2013.6557930","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557930","url":null,"abstract":"In some data mining applications the analyzed data can be classified as simultaneously belonging to more than one class, this characterizes the multi-label classification problem. Numerous methods for dealing with this problem are based on decomposition, which essentially treats labels (or some subsets of labels) independently and ignores interactions between them. This fact might be a problem, as some labels may be correlated to local patterns in the data. In this paper, we propose to enhance multi-label classifiers with the aid of biclusters, which are capable of finding the correlation between subsets of objects, features and labels. We then construct binary features from these patterns that can be interpreted as local correlations (in terms of subset of features and instances) in the data. These features are used as input for multi-label classifiers. We experimentally show that using such constructed features can improve the classification performance of some decompositive multi-label learning techniques.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133830440","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 : 2013-06-20DOI: 10.1109/CEC.2013.6557635
A. González-Pardo, David Camacho
Constraint Satisfaction Problems (CSP) have been widely studied in several research areas like Artificial Intelligence or Operational Research due their complexity and industrial interest. From previous research areas, heuristic (informed) search methods have been particularly active looking for feasible approaches. One of the critical problems to work with CSP is related to the exponential growth of computational resources needed to solve even the simplest problems. This paper presents a new efficient CSP graph-based representation to solve CSP by using Ant Colony Optimization (ACO) algorithms. This paper presents also a new heuristic (called Oblivion Rate), that have been designed to improve the current state-of-the-art in the application of ACO algorithms on these domains. The presented graph construction provides a strong reduction in both, the number of connections and the number of nodes needed to model the CSP. Also, the new heuristic is used to reduce the number of pheromones in the system (allowing to solve problems with an increasing complexity). This new approach has been tested, as case study, using the classical N-Queens Problem. Experimental results show how the new approach works in both, reducing the complexity of the resulting CSP graph and solving problems with increasing complexity through the utilization of the Oblivion Rate.
{"title":"A new CSP graph-based representation for Ant Colony Optimization","authors":"A. González-Pardo, David Camacho","doi":"10.1109/CEC.2013.6557635","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557635","url":null,"abstract":"Constraint Satisfaction Problems (CSP) have been widely studied in several research areas like Artificial Intelligence or Operational Research due their complexity and industrial interest. From previous research areas, heuristic (informed) search methods have been particularly active looking for feasible approaches. One of the critical problems to work with CSP is related to the exponential growth of computational resources needed to solve even the simplest problems. This paper presents a new efficient CSP graph-based representation to solve CSP by using Ant Colony Optimization (ACO) algorithms. This paper presents also a new heuristic (called Oblivion Rate), that have been designed to improve the current state-of-the-art in the application of ACO algorithms on these domains. The presented graph construction provides a strong reduction in both, the number of connections and the number of nodes needed to model the CSP. Also, the new heuristic is used to reduce the number of pheromones in the system (allowing to solve problems with an increasing complexity). This new approach has been tested, as case study, using the classical N-Queens Problem. Experimental results show how the new approach works in both, reducing the complexity of the resulting CSP graph and solving problems with increasing complexity through the utilization of the Oblivion Rate.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115551140","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 : 2013-06-20DOI: 10.1109/CEC.2013.6557850
A. A. Martins, A. Adewumi
Researchers have achieved varying levels of successes in proposing different methods to modify the particle's velocity updating formula for better performance of Particle Swarm Optimization (PSO). Variants of PSO that solved high-dimensional optimization problems up to 1,000 dimensions without losing superiority to its competitor(s) are rare. Meanwhile, high-dimensional real-world optimization problems are becoming realities hence PSO algorithm therefore needs some reworking to enhance it for better performance in handling such problems. This paper proposes a new PSO variant called Adaptive Velocity PSO (AV-PSO), which adaptively adjusts the velocity of particles based on Euclidean distance between the position of each particle and the position of the global best particle. To avoid getting trapped in local optimal, chaotic characteristics was introduced into the particle position updating formula. In all experiments, it is shown that AV-PSO is very efficient for solving low and high-dimensional global optimization problems. Empirical results show that AV-PSO outperformed AIWPSO, PSOrank, CRIW-PSO, def-PSO, e1-PSO and APSO. It also performed better than LSRS in many of the tested high-dimensional problems. AV-PSO was also used to optimize some high-dimensional problems with 4,000 dimensions with very good results.
{"title":"An Adaptive Velocity Particle Swarm Optimization for high-dimensional function optimization","authors":"A. A. Martins, A. Adewumi","doi":"10.1109/CEC.2013.6557850","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557850","url":null,"abstract":"Researchers have achieved varying levels of successes in proposing different methods to modify the particle's velocity updating formula for better performance of Particle Swarm Optimization (PSO). Variants of PSO that solved high-dimensional optimization problems up to 1,000 dimensions without losing superiority to its competitor(s) are rare. Meanwhile, high-dimensional real-world optimization problems are becoming realities hence PSO algorithm therefore needs some reworking to enhance it for better performance in handling such problems. This paper proposes a new PSO variant called Adaptive Velocity PSO (AV-PSO), which adaptively adjusts the velocity of particles based on Euclidean distance between the position of each particle and the position of the global best particle. To avoid getting trapped in local optimal, chaotic characteristics was introduced into the particle position updating formula. In all experiments, it is shown that AV-PSO is very efficient for solving low and high-dimensional global optimization problems. Empirical results show that AV-PSO outperformed AIWPSO, PSOrank, CRIW-PSO, def-PSO, e1-PSO and APSO. It also performed better than LSRS in many of the tested high-dimensional problems. AV-PSO was also used to optimize some high-dimensional problems with 4,000 dimensions with very good results.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117165303","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 : 2013-06-20DOI: 10.1109/CEC.2013.6557920
Kento Uemura, Naotoshi Nakashima, Y. Nagata, I. Ono
In this paper, we propose a new real-coded genetic algorithm (RCGA) for implicit constrained black-box function optimization. On implicit constrained problems, there often exist active constraints of which the optima lie on the boundaries, which makes the problem more difficult. Almost all of conventional constraint-handling techniques cannot be applied to implicit constrained black-box function optimization because we cannot get quantities of constraint violations and preference order of infeasible solutions. The resampling technique may be the only available choice to handle the implicit constraint. AREX/JGG is one of the most powerful RCGAs for non-constrained problems. However, AREX/JGG with resampling technique deteriorates on implicit constrained problems because few individuals are generated near the boundaries of active constraints and, thus, a population cannot approach the boundaries quickly. In order to find these optima, we believe that it is necessary to locate the mode of a distribution for generating new individuals nearer the boundaries. Since solutions around the optima on boundaries of active constraints may have better evaluation values, our proposed method employs the weighted mean of the best half individuals in a population as the mode of the distribution. We assess the proposed method through experiments with some benchmark problems and the results show the proposed method succeeds in finding the optimum with about 40-85% of function evaluations compared to AREX/JGG with resampling technique.
{"title":"A new real-coded genetic algorithm for implicit constrained black-box function optimization","authors":"Kento Uemura, Naotoshi Nakashima, Y. Nagata, I. Ono","doi":"10.1109/CEC.2013.6557920","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557920","url":null,"abstract":"In this paper, we propose a new real-coded genetic algorithm (RCGA) for implicit constrained black-box function optimization. On implicit constrained problems, there often exist active constraints of which the optima lie on the boundaries, which makes the problem more difficult. Almost all of conventional constraint-handling techniques cannot be applied to implicit constrained black-box function optimization because we cannot get quantities of constraint violations and preference order of infeasible solutions. The resampling technique may be the only available choice to handle the implicit constraint. AREX/JGG is one of the most powerful RCGAs for non-constrained problems. However, AREX/JGG with resampling technique deteriorates on implicit constrained problems because few individuals are generated near the boundaries of active constraints and, thus, a population cannot approach the boundaries quickly. In order to find these optima, we believe that it is necessary to locate the mode of a distribution for generating new individuals nearer the boundaries. Since solutions around the optima on boundaries of active constraints may have better evaluation values, our proposed method employs the weighted mean of the best half individuals in a population as the mode of the distribution. We assess the proposed method through experiments with some benchmark problems and the results show the proposed method succeeds in finding the optimum with about 40-85% of function evaluations compared to AREX/JGG with resampling technique.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123171917","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}
Differential evolution (DE) is a kind of evolutionary algorithms (EAs), which are population based heuristic global optimization methods. EAs, including DE, are usually criticized for their slow convergence comparing to traditional optimization methods. How to speed up the EA convergence while keeping its global search ability is still a challenge in the EA community. In this paper, we propose a differential evolution method with an orthogonal local search (OLSDE), which combines orthogonal design (OD) and EA for global optimization. In each generation of OLSDE, a general DE process is used firstly, and then an OD based local search is utilized to improve the quality of some solutions. The proposed OLSDE is applied to a variety of test instances and compared with a basic DE method and an orthogonal based DE method. The experimental results show that OLSDE is promising for dealing with the given continuous test instances.
{"title":"A differential evolution with an orthogonal local search","authors":"Zhenzhen Dai, Aimin Zhou, Guixu Zhang, Sanyi Jiang","doi":"10.1109/CEC.2013.6557847","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557847","url":null,"abstract":"Differential evolution (DE) is a kind of evolutionary algorithms (EAs), which are population based heuristic global optimization methods. EAs, including DE, are usually criticized for their slow convergence comparing to traditional optimization methods. How to speed up the EA convergence while keeping its global search ability is still a challenge in the EA community. In this paper, we propose a differential evolution method with an orthogonal local search (OLSDE), which combines orthogonal design (OD) and EA for global optimization. In each generation of OLSDE, a general DE process is used firstly, and then an OD based local search is utilized to improve the quality of some solutions. The proposed OLSDE is applied to a variety of test instances and compared with a basic DE method and an orthogonal based DE method. The experimental results show that OLSDE is promising for dealing with the given continuous test instances.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123175732","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 : 2013-06-20DOI: 10.1109/CEC.2013.6557625
Pedro B. Campos, David M. R. Lawson, S. Bale, James Alfred Walker, M. Trefzer, A. Tyrrell
This paper explores the potential for transistor level fault tolerance on a new Programmable Analogue and Digital Array (PAnDA) architecture1. In particular, this architecture features Combinatorial Configurable Analogue Blocks (CCABs) that can implement a number of combinatorial functions similar to FPGAs. In addition, PAnDA allows one to reconfigure features of the underlying analogue layer. In PAnDA-EINS, the functions that the CCAB can implement are predefined through the use of a routing block. This paper is a study of whether removing this routing block and allowing direct control of the transistors provides benefits for fault tolerance. Experiments are conducted in two stages. In the first stage, a logic function is evolved on a CCAB and then optimised using a GA. A fault is then injected into the substrate, breaking the logic function. The second stage of the experiment consists of evolving the logic function again on the faulty substrate. The results of these experiments show that the removal of the routing block from the CCAB is beneficial for fault tolerance.
{"title":"Overcoming faults using evolution on the PAnDA architecture","authors":"Pedro B. Campos, David M. R. Lawson, S. Bale, James Alfred Walker, M. Trefzer, A. Tyrrell","doi":"10.1109/CEC.2013.6557625","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557625","url":null,"abstract":"This paper explores the potential for transistor level fault tolerance on a new Programmable Analogue and Digital Array (PAnDA) architecture1. In particular, this architecture features Combinatorial Configurable Analogue Blocks (CCABs) that can implement a number of combinatorial functions similar to FPGAs. In addition, PAnDA allows one to reconfigure features of the underlying analogue layer. In PAnDA-EINS, the functions that the CCAB can implement are predefined through the use of a routing block. This paper is a study of whether removing this routing block and allowing direct control of the transistors provides benefits for fault tolerance. Experiments are conducted in two stages. In the first stage, a logic function is evolved on a CCAB and then optimised using a GA. A fault is then injected into the substrate, breaking the logic function. The second stage of the experiment consists of evolving the logic function again on the faulty substrate. The results of these experiments show that the removal of the routing block from the CCAB is beneficial for fault tolerance.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124780694","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 : 2013-06-20DOI: 10.1109/CEC.2013.6557988
Jan Hettenhausen, A. Lewis, M. Randall, T. Kipouros
The most common approach to decision making in muIti-objective optimisation with metaheuristics is a posteriori preference articulation. Increased model complexity and a gradual increase of optimisation problems with three or more objectives have revived an interest in progressively interactive decision making, where a human decision maker interacts with the algorithm at regular intervals. This paper presents an interactive approach to muIti-objective particle swarm optimisation (MOPSO) using a novel technique to preference articulation based on decision space interaction and visual preference articulation. The approach is tested on a 2D aerofoil design case study and comparisons are drawn to non-interactive MOPSO.
{"title":"Interactive multi-objective particle swarm optimisation using decision space interaction","authors":"Jan Hettenhausen, A. Lewis, M. Randall, T. Kipouros","doi":"10.1109/CEC.2013.6557988","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557988","url":null,"abstract":"The most common approach to decision making in muIti-objective optimisation with metaheuristics is a posteriori preference articulation. Increased model complexity and a gradual increase of optimisation problems with three or more objectives have revived an interest in progressively interactive decision making, where a human decision maker interacts with the algorithm at regular intervals. This paper presents an interactive approach to muIti-objective particle swarm optimisation (MOPSO) using a novel technique to preference articulation based on decision space interaction and visual preference articulation. The approach is tested on a 2D aerofoil design case study and comparisons are drawn to non-interactive MOPSO.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124904666","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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