Pub Date : 2013-06-20DOI: 10.1109/CEC.2013.6557948
S. Picek, D. Jakobović, M. Golub
Crossover is the most important operator in real-coded genetic algorithms. However, the choice of the best operator for a specific problem can be a difficult task. In this paper we compare 16 crossover operators on a set of 24 benchmark functions. A detailed statistical analysis is performed in an effort to find the best performing operators. The results show that there are significant differences in efficiency of different crossover operators, and that the efficiency may also depend on the distinctive properties of the fitness function. Additionally, the results point out that the combination of crossover operators yields the best results.
{"title":"On the recombination operator in the real-coded genetic algorithms","authors":"S. Picek, D. Jakobović, M. Golub","doi":"10.1109/CEC.2013.6557948","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557948","url":null,"abstract":"Crossover is the most important operator in real-coded genetic algorithms. However, the choice of the best operator for a specific problem can be a difficult task. In this paper we compare 16 crossover operators on a set of 24 benchmark functions. A detailed statistical analysis is performed in an effort to find the best performing operators. The results show that there are significant differences in efficiency of different crossover operators, and that the efficiency may also depend on the distinctive properties of the fitness function. Additionally, the results point out that the combination of crossover operators yields the best 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":"125168153","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.2013.6557805
Michal Pluhacek, R. Šenkeřík, I. Zelinka
In this paper, a novel strategy for particle swarm optimization is presented and investigated over its ability to improve the performance of PSO algorithm in the task of large scale optimization problems. This proposed strategy alters the way the velocity of each particle is determined. Promising results of this innovative strategy are presented in the results section and briefly analyzed.
{"title":"Investigation on the performance of a new multiple choice strategy for PSO Algorithm in the task of large scale optimization problems","authors":"Michal Pluhacek, R. Šenkeřík, I. Zelinka","doi":"10.1109/CEC.2013.6557805","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557805","url":null,"abstract":"In this paper, a novel strategy for particle swarm optimization is presented and investigated over its ability to improve the performance of PSO algorithm in the task of large scale optimization problems. This proposed strategy alters the way the velocity of each particle is determined. Promising results of this innovative strategy are presented in the results section and briefly analyzed.","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":"125800699","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.2013.6557869
Y. Kessaci, N. Melab, E. Talbi
In this paper, we deal with cloud brokering for the assignment optimization of VM requests in three-tier cloud infrastructures. We investigate the Pareto-based meta-heuristic approach to take into account multiple client and broker-centric optimization criteria. We propose a new multi-objective Genetic Algorithm (MOGA-CB ) that can be integrated in a cloud broker. Two objectives are considered in the optimization process: minimizing both the response time and the cost of the selected VM instances to satisfy the clients and to maximize the profit of the broker. The approach has been experimented using realistic data of different types of Amazon EC2 instances and their pricing history. The reported results show that MOGA-CB provides efficiently effective Pareto sets of solutions.
{"title":"A pareto-based genetic algorithm for optimized assignment of VM requests on a cloud brokering environment","authors":"Y. Kessaci, N. Melab, E. Talbi","doi":"10.1109/CEC.2013.6557869","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557869","url":null,"abstract":"In this paper, we deal with cloud brokering for the assignment optimization of VM requests in three-tier cloud infrastructures. We investigate the Pareto-based meta-heuristic approach to take into account multiple client and broker-centric optimization criteria. We propose a new multi-objective Genetic Algorithm (MOGA-CB ) that can be integrated in a cloud broker. Two objectives are considered in the optimization process: minimizing both the response time and the cost of the selected VM instances to satisfy the clients and to maximize the profit of the broker. The approach has been experimented using realistic data of different types of Amazon EC2 instances and their pricing history. The reported results show that MOGA-CB provides efficiently effective Pareto sets of solutions.","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":"123424209","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.2013.6557957
Anna I. Esparcia-Alcázar, Anaís Martínez-García, P. García-Sánchez, J. J. M. Guervós, A. García
In this work we address the problem of inventory and routing management in a retail chain. This involves the minimisation of two contradicting objectives, inventory holding costs and transportation costs, but which can be compounded in to a single one, the global costs. In previous work we addressed this using a single objective evolutionary algorithm but the duality inherent in the problem prompts us to consider a multiobjective approach; the aim is to determine what advantages each can bring. A number of experiments are carried out on several simulated and one real retail chain.
{"title":"Towards a multiobjective evolutionary approach to inventory and routing management in a retail chain","authors":"Anna I. Esparcia-Alcázar, Anaís Martínez-García, P. García-Sánchez, J. J. M. Guervós, A. García","doi":"10.1109/CEC.2013.6557957","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557957","url":null,"abstract":"In this work we address the problem of inventory and routing management in a retail chain. This involves the minimisation of two contradicting objectives, inventory holding costs and transportation costs, but which can be compounded in to a single one, the global costs. In previous work we addressed this using a single objective evolutionary algorithm but the duality inherent in the problem prompts us to consider a multiobjective approach; the aim is to determine what advantages each can bring. A number of experiments are carried out on several simulated and one real retail chain.","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":"123484806","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.2013.6557902
B. Kazimipour, Xiaodong Li, A. K. Qin
Several population initialization methods for evolutionary algorithms (EAs) have been proposed previously. This paper categorizes the most well-known initialization methods and studies the effect of them on large scale global optimization problems. Experimental results indicate that the optimization of large scale problems using EAs is more sensitive to the initial population than optimizing lower dimensional problems. Statistical analysis of results show that basic random number generators, which are the most commonly used method for population initialization in EAs, lead to the inferior performance. Furthermore, our study shows, regardless of the size of the initial population, choosing a proper initialization method is vital for solving large scale problems.
{"title":"Initialization methods for large scale global optimization","authors":"B. Kazimipour, Xiaodong Li, A. K. Qin","doi":"10.1109/CEC.2013.6557902","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557902","url":null,"abstract":"Several population initialization methods for evolutionary algorithms (EAs) have been proposed previously. This paper categorizes the most well-known initialization methods and studies the effect of them on large scale global optimization problems. Experimental results indicate that the optimization of large scale problems using EAs is more sensitive to the initial population than optimizing lower dimensional problems. Statistical analysis of results show that basic random number generators, which are the most commonly used method for population initialization in EAs, lead to the inferior performance. Furthermore, our study shows, regardless of the size of the initial population, choosing a proper initialization method is vital for solving large scale problems.","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":"123693195","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.2013.6557983
Hiroyuki Sato, Danushka Bollegala, Yoshihiko Hasegawa, H. Iba
Ranking the set of search results according to their relevance to a user query is an important task in an Information Retrieval (IR) systems such as a Web Search Engine. Learning the optimal ranking function for this task is a challenging problem because one must consider complex non-linear interactions between numerous factors such as the novelty, authority, contextual similarity, etc. of thousands of documents that contain the user query. We model this task as a non-linear ranking problem, for which we propose Rank-PMBGP, an efficient algorithm to learn an optimal non-linear ranking function using Probabilistic Model Building Genetic Programming. We evaluate the proposed method using the LETOR dataset, a standard benchmark dataset for training and evaluating ranking functions for IR. In our experiments, the proposed method obtains a Mean Average Precision (MAP) score of 0.291, thereby significantly outperforming a non-linear baseline approach that uses Genetic Programming.
{"title":"Learning non-linear ranking functions for web search using probabilistic model building GP","authors":"Hiroyuki Sato, Danushka Bollegala, Yoshihiko Hasegawa, H. Iba","doi":"10.1109/CEC.2013.6557983","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557983","url":null,"abstract":"Ranking the set of search results according to their relevance to a user query is an important task in an Information Retrieval (IR) systems such as a Web Search Engine. Learning the optimal ranking function for this task is a challenging problem because one must consider complex non-linear interactions between numerous factors such as the novelty, authority, contextual similarity, etc. of thousands of documents that contain the user query. We model this task as a non-linear ranking problem, for which we propose Rank-PMBGP, an efficient algorithm to learn an optimal non-linear ranking function using Probabilistic Model Building Genetic Programming. We evaluate the proposed method using the LETOR dataset, a standard benchmark dataset for training and evaluating ranking functions for IR. In our experiments, the proposed method obtains a Mean Average Precision (MAP) score of 0.291, thereby significantly outperforming a non-linear baseline approach that uses Genetic Programming.","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":"125509024","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.2013.6557865
A. Ohazulike, Ties Brands
Genetic algorithms (GAs) are widely accepted by researchers as a method of solving multi-objective optimization problems (MOPs), at least for listing a high quality approximation of the Pareto front of a MOP. In traffic management, it has been long established that tolls can be used to optimally distribute traffic in a network with aim of combating some traffic externalities such as congestion, emission, noise, safety issues. Formulating the multi-objective toll problem as a one point solution problem fails to give the general overview of the objective space of the MOP. Therefore, in this paper we develop a game theoretic approach that gives the general overview of the objective space of the multiobjective problem and compare the results with those of the wellknown genetic algorithm non-dominated sorting genetic algorithm II (NSGA-II). Results show that the game theoretic approach presents a promising tool for solving multi-objective problems, since it produces similar non-dominated solutions as NSGA-II, indicating that competing objectives (or stakeholders in the game setting) can still produce Pareto optimal solutions. Most fascinating is that a range of non-dominated solutions is generated during the game, and almost all generated solutions are in the neighborhood of the Pareto set. This indicates that good solutions are generated very fast during the game.
{"title":"Multi-objective optimization of traffic externalities using tolls","authors":"A. Ohazulike, Ties Brands","doi":"10.1109/CEC.2013.6557865","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557865","url":null,"abstract":"Genetic algorithms (GAs) are widely accepted by researchers as a method of solving multi-objective optimization problems (MOPs), at least for listing a high quality approximation of the Pareto front of a MOP. In traffic management, it has been long established that tolls can be used to optimally distribute traffic in a network with aim of combating some traffic externalities such as congestion, emission, noise, safety issues. Formulating the multi-objective toll problem as a one point solution problem fails to give the general overview of the objective space of the MOP. Therefore, in this paper we develop a game theoretic approach that gives the general overview of the objective space of the multiobjective problem and compare the results with those of the wellknown genetic algorithm non-dominated sorting genetic algorithm II (NSGA-II). Results show that the game theoretic approach presents a promising tool for solving multi-objective problems, since it produces similar non-dominated solutions as NSGA-II, indicating that competing objectives (or stakeholders in the game setting) can still produce Pareto optimal solutions. Most fascinating is that a range of non-dominated solutions is generated during the game, and almost all generated solutions are in the neighborhood of the Pareto set. This indicates that good solutions are generated very fast during the game.","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":"125537457","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.2013.6557554
R. Mukherjee, S. Debchoudhury, Rupam Kundu, Swagatam Das, P. N. Suganthan
Real life problems which deal with time varying landscape dynamics often pose serious challenge to the mettle of researchers in the domain of Evolutionary Computation. Classified as Dynamic Optimization problems (DOPs), these deal with candidate solutions which vary their dominance over dynamic change instances. The challenge is to efficiently recapture the dominant solution or the global optimum in each varying landscape. Differential Evolution (DE) algorithm with modifications of adaptability have been widely used to deal with the complexities of a dynamic landscape, yet problems persist unless dedicated structuring is done to exclusively deal with DOPs. In Adaptive Differential Evolution with Locality based Crossover (ADE-LbX) the mutation operation has been entrusted to a locality based scheme that retains traits of Euclidean distance based closest individuals around a potential solution. Diversity maintenance is further enhanced by incorporation of local best crossover scheme that renders the landscape independent of direction and empowers the algorithm with an explorative ability. An even distribution of solutions in different regions of landscape calls for a solution retention technique that adapts this algorithm to dynamism by using its previous information in diverse search domains. To evaluate the performance of ADE-LbX, it has been tested over Dynamic Problem instance proposed as in CEC 09 and compared with State-of-the-arts. The algorithm enjoys superior performance in varied problem configurations of the problem.
{"title":"Adaptive Differential Evolution with Locality based Crossover for Dynamic Optimization","authors":"R. Mukherjee, S. Debchoudhury, Rupam Kundu, Swagatam Das, P. N. Suganthan","doi":"10.1109/CEC.2013.6557554","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557554","url":null,"abstract":"Real life problems which deal with time varying landscape dynamics often pose serious challenge to the mettle of researchers in the domain of Evolutionary Computation. Classified as Dynamic Optimization problems (DOPs), these deal with candidate solutions which vary their dominance over dynamic change instances. The challenge is to efficiently recapture the dominant solution or the global optimum in each varying landscape. Differential Evolution (DE) algorithm with modifications of adaptability have been widely used to deal with the complexities of a dynamic landscape, yet problems persist unless dedicated structuring is done to exclusively deal with DOPs. In Adaptive Differential Evolution with Locality based Crossover (ADE-LbX) the mutation operation has been entrusted to a locality based scheme that retains traits of Euclidean distance based closest individuals around a potential solution. Diversity maintenance is further enhanced by incorporation of local best crossover scheme that renders the landscape independent of direction and empowers the algorithm with an explorative ability. An even distribution of solutions in different regions of landscape calls for a solution retention technique that adapts this algorithm to dynamism by using its previous information in diverse search domains. To evaluate the performance of ADE-LbX, it has been tested over Dynamic Problem instance proposed as in CEC 09 and compared with State-of-the-arts. The algorithm enjoys superior performance in varied problem configurations of the problem.","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":"116571744","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.2013.6557889
Harith Al-Sahaf, A. Song, Mengjie Zhang
In this paper, we propose a novel hybrid classification method which is based on two distinct approaches, namely Genetic Programming (GP) and Nearest Neighbour (kNN). The method relies on a memory list which contains some correctly labelled instances and is formed by classifiers evolved by GP. The class label of a new instance will be determined by combining its most similar instances in the memory list and the output of GP classifier on this instance. The results show that this proposed method can outperform conventional GP-based classification approach. Compared with conventional classification methods such as Naive Bayes, SVM, Decision Trees, and conventional kNN, this method can also achieve better or comparable accuracies on a set of binary problems. The evaluation cost of this hybrid method is much lower than that of conventional kNN.
{"title":"Hybridisation of Genetic Programming and Nearest Neighbour for classification","authors":"Harith Al-Sahaf, A. Song, Mengjie Zhang","doi":"10.1109/CEC.2013.6557889","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557889","url":null,"abstract":"In this paper, we propose a novel hybrid classification method which is based on two distinct approaches, namely Genetic Programming (GP) and Nearest Neighbour (kNN). The method relies on a memory list which contains some correctly labelled instances and is formed by classifiers evolved by GP. The class label of a new instance will be determined by combining its most similar instances in the memory list and the output of GP classifier on this instance. The results show that this proposed method can outperform conventional GP-based classification approach. Compared with conventional classification methods such as Naive Bayes, SVM, Decision Trees, and conventional kNN, this method can also achieve better or comparable accuracies on a set of binary problems. The evaluation cost of this hybrid method is much lower than that of conventional kNN.","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":"122990340","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.2013.6557652
N. Geng, D. Gong, Yong Zhang
In order to solve the problem of path planning in an environment with many terrains, we propose a method based on interval multi-objective Particle Swarm Optimization (PSO). First, the environment is modeled by the line partition method, and then, according to the distribution of the polygonal lines which form the robot path and taking the velocity's disturbance into consideration, robot's passing time is formulated as an interval by combining Local Optimal Criterion (LOC), and the path's danger degree is estimated through the area ratio between the robot path and the danger source. In addition, the path length is also calculated as an optimization objective. As a result, the robot path planning problem is modeled as an optimization problem with three objectives. Finally, the interval multiobjective PSO is employed to solve the problem above. Simulation and experimental results verify the effectiveness of the proposed method.
{"title":"Robot path planning in an environment with many terrains based on interval multi-objective PSO","authors":"N. Geng, D. Gong, Yong Zhang","doi":"10.1109/CEC.2013.6557652","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557652","url":null,"abstract":"In order to solve the problem of path planning in an environment with many terrains, we propose a method based on interval multi-objective Particle Swarm Optimization (PSO). First, the environment is modeled by the line partition method, and then, according to the distribution of the polygonal lines which form the robot path and taking the velocity's disturbance into consideration, robot's passing time is formulated as an interval by combining Local Optimal Criterion (LOC), and the path's danger degree is estimated through the area ratio between the robot path and the danger source. In addition, the path length is also calculated as an optimization objective. As a result, the robot path planning problem is modeled as an optimization problem with three objectives. Finally, the interval multiobjective PSO is employed to solve the problem above. Simulation and experimental results verify the effectiveness of the proposed method.","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":"114274518","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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