Pub Date : 2012-06-10DOI: 10.1109/CEC.2012.6256101
H. Abbass, D. Essam, R. Sarker
Bringing the 2012 IEEE World Congress on Computational Intelligence (IEEE-WCCI 2012) for the first time to Australia has been a fulfilling journey of joy and honour. This premier event of the IEEE Computational Intelligence Society (IEEE-CIS) brings together three flagship conferences of the society in even years. It consisted of these conferences: the International Joint Conference on Neural Networks (IJCNN 2012), the IEEE International Conference on Fuzzy Systems (FUZZIEEE 2012) and the 2012 IEEE Congress on Evolutionary Computation (IEEE CEC 2012). This document presents the technical papers from the IEEE CEC 2012 conference, which had 758 submissions, of which, 482 were accepted.
{"title":"2012 IEEE Congress on Evolutionary Computation","authors":"H. Abbass, D. Essam, R. Sarker","doi":"10.1109/CEC.2012.6256101","DOIUrl":"https://doi.org/10.1109/CEC.2012.6256101","url":null,"abstract":"Bringing the 2012 IEEE World Congress on Computational Intelligence (IEEE-WCCI 2012) for the first time to Australia has been a fulfilling journey of joy and honour. This premier event of the IEEE Computational Intelligence Society (IEEE-CIS) brings together three flagship conferences of the society in even years. It consisted of these conferences: the International Joint Conference on Neural Networks (IJCNN 2012), the IEEE International Conference on Fuzzy Systems (FUZZIEEE 2012) and the 2012 IEEE Congress on Evolutionary Computation (IEEE CEC 2012). This document presents the technical papers from the IEEE CEC 2012 conference, which had 758 submissions, of which, 482 were accepted.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"7 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2012-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86727209","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 : 2012-01-01DOI: 10.1109/CEC.2012.6253009
Özgür Ülker, Dario Landa Silva
Office Space Allocation (OSA) is the task of correctly allocating the spatial resources of an institution to a set of entities by minimising the wastage of space and the violation of additional constraints. In this paper, an evolutionary local search algorithm is presented to tackle this problem. The evolutionary components of the algorithm include standard crossover and mutation operators and a relatively small population of individuals. The offspring produced by the evolutionary operators are subjected to a short but intense local search process. A very fast cost calculation method tailored for searching a large section of the search space is implemented. Extensive experimentation is carried out related to several parameters of the algorithm: the mutation rate, the population size, the length of the local search procedure after each mutation, hence the balance between the evolutionary and the local search stages, and the level of greediness of the local search process. The final results on 72 different data instances show that this hybrid evolutionary algorithm is very competitive with an integer programming model.
{"title":"Evolutionary local search for solving the office space allocation problem","authors":"Özgür Ülker, Dario Landa Silva","doi":"10.1109/CEC.2012.6253009","DOIUrl":"https://doi.org/10.1109/CEC.2012.6253009","url":null,"abstract":"Office Space Allocation (OSA) is the task of correctly allocating the spatial resources of an institution to a set of entities by minimising the wastage of space and the violation of additional constraints. In this paper, an evolutionary local search algorithm is presented to tackle this problem. The evolutionary components of the algorithm include standard crossover and mutation operators and a relatively small population of individuals. The offspring produced by the evolutionary operators are subjected to a short but intense local search process. A very fast cost calculation method tailored for searching a large section of the search space is implemented. Extensive experimentation is carried out related to several parameters of the algorithm: the mutation rate, the population size, the length of the local search procedure after each mutation, hence the balance between the evolutionary and the local search stages, and the level of greediness of the local search process. The final results on 72 different data instances show that this hybrid evolutionary algorithm is very competitive with an integer programming model.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"2 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79151552","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 : 2012-01-01DOI: 10.1109/CEC.2012.6256618
Fei-yue Qiu, Yu-shi Wu, Liping Wang, Bo Jiang
{"title":"Bipolar preferences dominance based evolutionary algorithm for many-objective optimization","authors":"Fei-yue Qiu, Yu-shi Wu, Liping Wang, Bo Jiang","doi":"10.1109/CEC.2012.6256618","DOIUrl":"https://doi.org/10.1109/CEC.2012.6256618","url":null,"abstract":"","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"8 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83247737","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 : 2011-06-05DOI: 10.1109/CEC.2011.5949589
N. Krasnogor
In this talk I will overview ten years of research in the application of evolutionary computation ideas in the natural sciences. The talk will take us on a tour that will cover problems in nanoscience, e.g. controlling self-organizing systems, optimizing scanning probe microscopy, etc., problems arising in bioinformatics, such as predicting protein structures and their features, to challenges emerging in systems and synthetic biology. Although the algorithmic solutions involved in these problems are different from each other, at their core, they retain Darwin's wonderful insights. I will conclude the talk by giving a personal view on why EC has been so successful and where, in my mind, the future lies.
{"title":"Darwin's magic: Evolutionary computation in nanoscience, bioinformatics and systems biology","authors":"N. Krasnogor","doi":"10.1109/CEC.2011.5949589","DOIUrl":"https://doi.org/10.1109/CEC.2011.5949589","url":null,"abstract":"In this talk I will overview ten years of research in the application of evolutionary computation ideas in the natural sciences. The talk will take us on a tour that will cover problems in nanoscience, e.g. controlling self-organizing systems, optimizing scanning probe microscopy, etc., problems arising in bioinformatics, such as predicting protein structures and their features, to challenges emerging in systems and synthetic biology. Although the algorithmic solutions involved in these problems are different from each other, at their core, they retain Darwin's wonderful insights. I will conclude the talk by giving a personal view on why EC has been so successful and where, in my mind, the future lies.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"2012 1","pages":"1-3"},"PeriodicalIF":0.0,"publicationDate":"2011-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74066390","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 : 2010-09-27DOI: 10.1109/CEC.2010.5586497
E. Murphy, M. O’Neill, E. López, A. Brabazon
In this paper we investigate the application of tree-adjunct grammars to grammatical evolution. The standard type of grammar used by grammatical evolution, context-free grammars, produce a subset of the languages that tree-adjunct grammars can produce, making tree-adjunct grammars, expressively, more powerful. In this study we shed some light on the effects of tree-adjunct grammars on grammatical evolution, or tree-adjunct grammatical evolution. We perform an analytic comparison of the performance of both setups, i.e., grammatical evolution and tree-adjunct grammatical evolution, across a number of classic genetic programming benchmarking problems. The results firmly indicate that tree-adjunct grammatical evolution has a better overall performance (measured in terms of finding the global optima).
{"title":"Tree-adjunct grammatical evolution","authors":"E. Murphy, M. O’Neill, E. López, A. Brabazon","doi":"10.1109/CEC.2010.5586497","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586497","url":null,"abstract":"In this paper we investigate the application of tree-adjunct grammars to grammatical evolution. The standard type of grammar used by grammatical evolution, context-free grammars, produce a subset of the languages that tree-adjunct grammars can produce, making tree-adjunct grammars, expressively, more powerful. In this study we shed some light on the effects of tree-adjunct grammars on grammatical evolution, or tree-adjunct grammatical evolution. We perform an analytic comparison of the performance of both setups, i.e., grammatical evolution and tree-adjunct grammatical evolution, across a number of classic genetic programming benchmarking problems. The results firmly indicate that tree-adjunct grammatical evolution has a better overall performance (measured in terms of finding the global optima).","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"10 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2010-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90175141","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 : 2010-09-27DOI: 10.1109/CEC.2010.5586229
H. Handa, H. Kawakami
The optimization of the number and the alignment of sensors is quite important task for designing intelligent agents/robotics. Even though we could use excellent learning algorithms, it will not work well if the alignment of sensors is wrong or the number of sensors is not enough. In addition, if a large number of sensors are available, it will cause the delay of learning. In this paper, we propose the use of Manifold Learning for Evolutionary Learning with redundant sensory inputs in order to avoid the difficulty of designing the allocation of sensors. The proposed method is composed of two stages: The first stage is to generate a mapping from higher dimensional sensory inputs to lower dimensional space, by using Manifold Learning. The second stage is using Evolutionary Learning to learn control scheme. The input data for Evolutionary Learning is generated by translating sensory inputs into lower dimensional data by using the mapping.
{"title":"Dimension reduction by Manifold Learning for Evolutionary Learning with redundant sensory inputs","authors":"H. Handa, H. Kawakami","doi":"10.1109/CEC.2010.5586229","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586229","url":null,"abstract":"The optimization of the number and the alignment of sensors is quite important task for designing intelligent agents/robotics. Even though we could use excellent learning algorithms, it will not work well if the alignment of sensors is wrong or the number of sensors is not enough. In addition, if a large number of sensors are available, it will cause the delay of learning. In this paper, we propose the use of Manifold Learning for Evolutionary Learning with redundant sensory inputs in order to avoid the difficulty of designing the allocation of sensors. The proposed method is composed of two stages: The first stage is to generate a mapping from higher dimensional sensory inputs to lower dimensional space, by using Manifold Learning. The second stage is using Evolutionary Learning to learn control scheme. The input data for Evolutionary Learning is generated by translating sensory inputs into lower dimensional data by using the mapping.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"7 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2010-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87010077","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 : 2010-09-27DOI: 10.1109/CEC.2010.5586500
F. Silva, J. M. Sánchez-Pérez, J. Pulido, M. A. Vega-Rodríguez
Despite of being a very common task in bioinformatics, multiple sequence alignment is not a trivial matter. Arranging a set of molecular sequences to reveal their similarities and their differences is often hardened by the complexity and the size of the search space involved, which undermine the approaches that try to explore exhaustively the solution's search space. Due to its nature, Genetic Algorithms, which are prone for general combinatorial problems optimization in large and complex search spaces, emerge as serious candidates to tackle with the multiple sequence alignment problem. We have developed an evolutionary approach, AlineaGA, which uses a Genetic Algorithm with local search optimization embedded on its mutation operators for performing multiple sequence alignment. Now, we have enhanced its selection method by employing an elitist strategy, and we have also developed a new crossover operator. These transformations allow AlineaGA to improve its robustness and to obtain better fit solutions. Also, we have studied the effect of the mutation probability in solutions' evolution by analyzing the performance of the whole population throughout generations. We conclude that increasing the mutation probability leads to better solutions in fewer generations and that the mutation operators have a dramatic effect in this particular domain.
{"title":"An evolutionary approach for performing multiple sequence alignment","authors":"F. Silva, J. M. Sánchez-Pérez, J. Pulido, M. A. Vega-Rodríguez","doi":"10.1109/CEC.2010.5586500","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586500","url":null,"abstract":"Despite of being a very common task in bioinformatics, multiple sequence alignment is not a trivial matter. Arranging a set of molecular sequences to reveal their similarities and their differences is often hardened by the complexity and the size of the search space involved, which undermine the approaches that try to explore exhaustively the solution's search space. Due to its nature, Genetic Algorithms, which are prone for general combinatorial problems optimization in large and complex search spaces, emerge as serious candidates to tackle with the multiple sequence alignment problem. We have developed an evolutionary approach, AlineaGA, which uses a Genetic Algorithm with local search optimization embedded on its mutation operators for performing multiple sequence alignment. Now, we have enhanced its selection method by employing an elitist strategy, and we have also developed a new crossover operator. These transformations allow AlineaGA to improve its robustness and to obtain better fit solutions. Also, we have studied the effect of the mutation probability in solutions' evolution by analyzing the performance of the whole population throughout generations. We conclude that increasing the mutation probability leads to better solutions in fewer generations and that the mutation operators have a dramatic effect in this particular domain.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"2012 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2010-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89858919","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 : 2010-07-18DOI: 10.1109/CEC.2010.5586160
M. G. Arenas, J. L. Laredo, P. Castillo, P. García-Sánchez, A. García, A. Prieto, J. J. M. Guervós
This paper proposes using the ANOVA (ANalysis Of the VAriance) method to carry out an exhaustive analysis of the simulated annealing (Sim-Ann) method and the different parameters it requires, such as those related to: the neighbourhood; the cooling scheme; the initial temperature; the number of times the cooling scheme is applied; and the number of times we search for best individual before the temperature is cooled. When undertaking a detailed statistical analysis of the influence of each parameter, the designer should pay attention mostly to the parameter presenting values that are statistically most significant. Following this idea, the significance and relative importance of the parameters with respect to the obtained results, as well as suitable values for each of these, were obtained using ANOVA on four well known function optimization problems.
{"title":"Statistical analysis of the parameters of the simulated annealing algorithm","authors":"M. G. Arenas, J. L. Laredo, P. Castillo, P. García-Sánchez, A. García, A. Prieto, J. J. M. Guervós","doi":"10.1109/CEC.2010.5586160","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586160","url":null,"abstract":"This paper proposes using the ANOVA (ANalysis Of the VAriance) method to carry out an exhaustive analysis of the simulated annealing (Sim-Ann) method and the different parameters it requires, such as those related to: the neighbourhood; the cooling scheme; the initial temperature; the number of times the cooling scheme is applied; and the number of times we search for best individual before the temperature is cooled. When undertaking a detailed statistical analysis of the influence of each parameter, the designer should pay attention mostly to the parameter presenting values that are statistically most significant. Following this idea, the significance and relative importance of the parameters with respect to the obtained results, as well as suitable values for each of these, were obtained using ANOVA on four well known function optimization problems.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"37 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73878796","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 : 2010-07-18DOI: 10.1109/CEC.2010.5586311
J. Berger, Khaled Jabeur, A. Boukhtouta, A. Guitouni, A. Ghanmi
Efficient vehicle path planning in hostile environment to carry out rescue or tactical logistic missions remains very challenging. Most approaches reported so far relies on key assumptions and heuristic procedures to reduce problem complexity. In this paper, a new model and a hybrid genetic algorithm are proposed to solve the rescue path planning problem for a single vehicle navigating in uncertain adversarial environment. We present a simplified mathematical linear programming formulation aimed at minimizing traveled distance and threat exposure. As an approximation to the basic problem, the user-defined model allows to specify a lower bound on the optimal solution for some particular survivability conditions. Hard problem instances are then solved using a novel hybrid genetic algorithm relaxing some of the common assumptions considered by previous path construction methods. The algorithm evolves a population of solution combining genetic operators with a new stochastic path generation technique, providing guided local search, while improving solution quality. The value of the problem-solving approach is shown for simple cases and compared to an alternate heuristic.
{"title":"A hybrid genetic algorithm for rescue path planning in uncertain adversarial environment","authors":"J. Berger, Khaled Jabeur, A. Boukhtouta, A. Guitouni, A. Ghanmi","doi":"10.1109/CEC.2010.5586311","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586311","url":null,"abstract":"Efficient vehicle path planning in hostile environment to carry out rescue or tactical logistic missions remains very challenging. Most approaches reported so far relies on key assumptions and heuristic procedures to reduce problem complexity. In this paper, a new model and a hybrid genetic algorithm are proposed to solve the rescue path planning problem for a single vehicle navigating in uncertain adversarial environment. We present a simplified mathematical linear programming formulation aimed at minimizing traveled distance and threat exposure. As an approximation to the basic problem, the user-defined model allows to specify a lower bound on the optimal solution for some particular survivability conditions. Hard problem instances are then solved using a novel hybrid genetic algorithm relaxing some of the common assumptions considered by previous path construction methods. The algorithm evolves a population of solution combining genetic operators with a new stochastic path generation technique, providing guided local search, while improving solution quality. The value of the problem-solving approach is shown for simple cases and compared to an alternate heuristic.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"6 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75853047","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 : 2010-07-18DOI: 10.1109/CEC.2010.5586317
Xiaodong Li, K. Deb
Niching is an important technique for multimodal optimization in Evolutionary Computation. Most existing niching algorithms are evaluated using only 1 or 2 dimensional multimodal functions. However, it remains unclear how these niching algorithms perform on higher dimensional multimodal problems. This paper compares several schemes of PSO update rules, and examines the effects of incorporating these schemes into a lbest PSO niching algorithm using a ring topology. Subsequently a new Cauchy and Gaussian distributions based PSO (CGPSO) is proposed. Our experiments suggest that CGPSO seems to be able to locate more global peaks than other PSO variants on multimodal functions which typically have many global peaks but very few local peaks.
{"title":"Comparing lbest PSO niching algorithms using different position update rules","authors":"Xiaodong Li, K. Deb","doi":"10.1109/CEC.2010.5586317","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586317","url":null,"abstract":"Niching is an important technique for multimodal optimization in Evolutionary Computation. Most existing niching algorithms are evaluated using only 1 or 2 dimensional multimodal functions. However, it remains unclear how these niching algorithms perform on higher dimensional multimodal problems. This paper compares several schemes of PSO update rules, and examines the effects of incorporating these schemes into a lbest PSO niching algorithm using a ring topology. Subsequently a new Cauchy and Gaussian distributions based PSO (CGPSO) is proposed. Our experiments suggest that CGPSO seems to be able to locate more global peaks than other PSO variants on multimodal functions which typically have many global peaks but very few local peaks.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"12 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75492861","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：