Pub Date : 2013-06-20DOI: 10.1109/CEC.2013.6557578
P. Jamieson, Farnaz Gharibian, Lesley Shannon
Supergenes are an addition to a genetic algorithm's genome that duplicate genes in the genome, represent local optimizations, and have the potential to be expressed overriding the duplicated gene. We introduce supergenes in a genetic algorithm for FPGA placement where a placement algorithm places a mix of fine-grain components and medium-grain components (where a medium-grain component is 2 to 10 times the size of a finegrain component). This is the first placement algorithm, to our knowledge, that can deal with such a mix of components. Our results show that supergenes improve a placement metric (clock speed of the FPGA) by approximately 10%. We also show and explore mutation operators on supergenes, and we experimentally demonstrate that the expression of a supergene can be effectively controlled via a binary function for our placement problem.
{"title":"Supergenes in a genetic algorithm for heterogeneous FPGA placement","authors":"P. Jamieson, Farnaz Gharibian, Lesley Shannon","doi":"10.1109/CEC.2013.6557578","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557578","url":null,"abstract":"Supergenes are an addition to a genetic algorithm's genome that duplicate genes in the genome, represent local optimizations, and have the potential to be expressed overriding the duplicated gene. We introduce supergenes in a genetic algorithm for FPGA placement where a placement algorithm places a mix of fine-grain components and medium-grain components (where a medium-grain component is 2 to 10 times the size of a finegrain component). This is the first placement algorithm, to our knowledge, that can deal with such a mix of components. Our results show that supergenes improve a placement metric (clock speed of the FPGA) by approximately 10%. We also show and explore mutation operators on supergenes, and we experimentally demonstrate that the expression of a supergene can be effectively controlled via a binary function for our placement 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":"129485424","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.2013.6557936
B. A. Júnior, P. Pinheiro, R. D. Saraiva
This paper addresses the Irregular Strip Packing problem, a particular case of Cutting and Packing problems in which a set of polygons has to be packed within a rectangular object. To identify good quality solutions, we propose a hybrid methodology based on a meta-heuristic engine (i.e., Genetic Algorithm) and a well known placement heuristic called Bottom-Left. In addition, differently from several approaches presented in the literature, we investigate the application of the No-fit Polygon as a placement tool for obtaining local optima. The results are further improved by a shrinking algorithm that works within the meta-heuristic component. To assess the potentials of the proposed methodology, computational experiments performed on a set of difficult benchmark instances of the Irregular Strip Packing problem are discussed here for evaluation purposes.
{"title":"Tackling the Irregular Strip Packing problem by hybridizing genetic algorithm and bottom-left heuristic","authors":"B. A. Júnior, P. Pinheiro, R. D. Saraiva","doi":"10.1109/CEC.2013.6557936","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557936","url":null,"abstract":"This paper addresses the Irregular Strip Packing problem, a particular case of Cutting and Packing problems in which a set of polygons has to be packed within a rectangular object. To identify good quality solutions, we propose a hybrid methodology based on a meta-heuristic engine (i.e., Genetic Algorithm) and a well known placement heuristic called Bottom-Left. In addition, differently from several approaches presented in the literature, we investigate the application of the No-fit Polygon as a placement tool for obtaining local optima. The results are further improved by a shrinking algorithm that works within the meta-heuristic component. To assess the potentials of the proposed methodology, computational experiments performed on a set of difficult benchmark instances of the Irregular Strip Packing problem are discussed here for evaluation purposes.","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":"128186718","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.2013.6557801
V. Lopez, E. Sánchez, A. Alanis
In this paper, a discrete-time inverse optimal control is applied to a three-phase linear induction motor (LIM) in order to achieve trajectory tracking of a position reference. An online neural identifier, built using a recurrent high-order neural network (RHONN) trained with the Extended Kalman Filter (EKF), is employed in order to model the system. The control law calculates the input voltage signals which are inverse optimal in the sense that they minimize a cost functional without solving the Hamilton-Jacobi-Bellman (HJB) equation. Particle Swarm Optimization (PSO) algorithm is employed in order to improve identification and control performance. The applicability of the proposed control scheme is illustrated via simulations.
{"title":"PSO neural inverse optimal control for a linear induction motor","authors":"V. Lopez, E. Sánchez, A. Alanis","doi":"10.1109/CEC.2013.6557801","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557801","url":null,"abstract":"In this paper, a discrete-time inverse optimal control is applied to a three-phase linear induction motor (LIM) in order to achieve trajectory tracking of a position reference. An online neural identifier, built using a recurrent high-order neural network (RHONN) trained with the Extended Kalman Filter (EKF), is employed in order to model the system. The control law calculates the input voltage signals which are inverse optimal in the sense that they minimize a cost functional without solving the Hamilton-Jacobi-Bellman (HJB) equation. Particle Swarm Optimization (PSO) algorithm is employed in order to improve identification and control performance. The applicability of the proposed control scheme is illustrated via simulations.","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":"128681661","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.2013.6557971
Adriano Soares Koshiyama, Tatiana Escovedo, D. Dias, M. Vellasco, R. Tanscheit
This work presents a Genetic Fuzzy Classification System (GFCS) called Genetic Programming Fuzzy Classification System (GPF-CLASS). This model differs from the traditional approach of GFCS, which uses the metaheuristic as a way to learn “if-then” fuzzy rules. This classical approach needs several changes and constraints on the use of genetic operators, evaluation and selection, which depends primarily on the metaheuristic used. Genetic Programming makes this implementation costly and explores few of its characteristics and potentialities. The GPF-CLASS model seeks for a greater integration with the metaheuristic: Multi-Gene Genetic Programming (MGGP), exploring its potential of terminals selection (input features) and functional form and at the same time aims to provide the user with a comprehension of the classification solution. Tests with 22 benchmarks datasets for classification have been performed and, as well as statistical analysis and comparisons with others Genetic Fuzzy Systems proposed in the literature.
{"title":"GPF-CLASS: A Genetic Fuzzy model for classification","authors":"Adriano Soares Koshiyama, Tatiana Escovedo, D. Dias, M. Vellasco, R. Tanscheit","doi":"10.1109/CEC.2013.6557971","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557971","url":null,"abstract":"This work presents a Genetic Fuzzy Classification System (GFCS) called Genetic Programming Fuzzy Classification System (GPF-CLASS). This model differs from the traditional approach of GFCS, which uses the metaheuristic as a way to learn “if-then” fuzzy rules. This classical approach needs several changes and constraints on the use of genetic operators, evaluation and selection, which depends primarily on the metaheuristic used. Genetic Programming makes this implementation costly and explores few of its characteristics and potentialities. The GPF-CLASS model seeks for a greater integration with the metaheuristic: Multi-Gene Genetic Programming (MGGP), exploring its potential of terminals selection (input features) and functional form and at the same time aims to provide the user with a comprehension of the classification solution. Tests with 22 benchmarks datasets for classification have been performed and, as well as statistical analysis and comparisons with others Genetic Fuzzy Systems proposed in the literature.","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":"127387506","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.2013.6557731
S. Doncieux, Jean-Baptiste Mouret
Recent results in evolutionary robotics show that explicitly encouraging the behavioral diversity of candidate solutions drastically improves the convergence of many experiments. The performance of this technique depends, however, on the choice of a behavioral similarity measure (BSM). Here we propose that the experimenter does not actually need to choose: provided that several similarity measures are conceivable, using them all could lead to better results than choosing a single one. Values computed by several BSM can be averaged, which is computationally expensive because it requires the computation of all the BSM at each generation, or randomly switched at a user-chosen frequency, which is a cheaper alternative. We compare these two approaches in two experimental setups - a ball collecting task and hexapod locomotion - with five different BSMs. Results show that (1) using several BSM in a single run increases the performance while avoiding the need to choose the most appropriate BSM and (2) switching between BSMs leads to better results than taking the mean behavioral diversity, while requiring less computational power.
{"title":"Behavioral diversity with multiple behavioral distances","authors":"S. Doncieux, Jean-Baptiste Mouret","doi":"10.1109/CEC.2013.6557731","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557731","url":null,"abstract":"Recent results in evolutionary robotics show that explicitly encouraging the behavioral diversity of candidate solutions drastically improves the convergence of many experiments. The performance of this technique depends, however, on the choice of a behavioral similarity measure (BSM). Here we propose that the experimenter does not actually need to choose: provided that several similarity measures are conceivable, using them all could lead to better results than choosing a single one. Values computed by several BSM can be averaged, which is computationally expensive because it requires the computation of all the BSM at each generation, or randomly switched at a user-chosen frequency, which is a cheaper alternative. We compare these two approaches in two experimental setups - a ball collecting task and hexapod locomotion - with five different BSMs. Results show that (1) using several BSM in a single run increases the performance while avoiding the need to choose the most appropriate BSM and (2) switching between BSMs leads to better results than taking the mean behavioral diversity, while requiring less computational power.","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":"130039336","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.2013.6557985
Kazuhisa Chiba
Design Informatics has three points of view. First point is the efficient exploration in design space using evolutionary computation. Second point is the structurization and visualization of design space using data mining. Third point is the application to practical problems. In the present study, the influence of the seven pure and hybrid optimizers for design information has been investigated in order to explain the selection manner of optimizer for data mining. A single-stage hybrid rocket design problem is picked up as the present design object. As a result, mining result depends on not the number of generation (convergence) but the optimizers (diversity). Consequently, the optimizer with diversity performance should be selected in order to obtain global design information in the design space. Therefore, the diversity performance has also been explained for the seven optimization methods by using three standard mathematical test problems with/without noise. The result indicates that the hybrid method between the differential evolution and the genetic algorithm is beneficial performance for efficient exploration in the design space under the condition for large-scale design problems within 102 order evolution at most. Moreover, the comparison among eight crossovers indicates that the principal component analysis blended crossover is good selection on the hybrid method between the differential evolution and the genetic algorithm.
{"title":"Evolutionary hybrid computation in view of design information by data mining","authors":"Kazuhisa Chiba","doi":"10.1109/CEC.2013.6557985","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557985","url":null,"abstract":"Design Informatics has three points of view. First point is the efficient exploration in design space using evolutionary computation. Second point is the structurization and visualization of design space using data mining. Third point is the application to practical problems. In the present study, the influence of the seven pure and hybrid optimizers for design information has been investigated in order to explain the selection manner of optimizer for data mining. A single-stage hybrid rocket design problem is picked up as the present design object. As a result, mining result depends on not the number of generation (convergence) but the optimizers (diversity). Consequently, the optimizer with diversity performance should be selected in order to obtain global design information in the design space. Therefore, the diversity performance has also been explained for the seven optimization methods by using three standard mathematical test problems with/without noise. The result indicates that the hybrid method between the differential evolution and the genetic algorithm is beneficial performance for efficient exploration in the design space under the condition for large-scale design problems within 102 order evolution at most. Moreover, the comparison among eight crossovers indicates that the principal component analysis blended crossover is good selection on the hybrid method between the differential evolution and the genetic algorithm.","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":"129084851","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.2013.6557880
Francois Legillon, N. Melab, Didier Renard, E. Talbi
Public cloud computing allows one to rent virtual servers on a hourly basis. This raises the problematic of being able to decide which server offers to take, which providers to use, and how to use them to acquire sufficient service capacity, while maintaining a cost effective platform. This article proposes a new realistic model to tackle the problem, placing services into IAAS virtual machines from multiple providers. A flexible protocol is defined to generate real-life instances, and applied on two industrial cases with four real cloud providers. An evolutionary approach, with new specific operators, is introduced and compared to a MIP formulation. Experiments conducted on two data-sets show that the evolutionary approach is viable to tackle real-size instances in reasonable amount of time.
{"title":"Cost minimization of service deployment in a multi-cloud environment","authors":"Francois Legillon, N. Melab, Didier Renard, E. Talbi","doi":"10.1109/CEC.2013.6557880","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557880","url":null,"abstract":"Public cloud computing allows one to rent virtual servers on a hourly basis. This raises the problematic of being able to decide which server offers to take, which providers to use, and how to use them to acquire sufficient service capacity, while maintaining a cost effective platform. This article proposes a new realistic model to tackle the problem, placing services into IAAS virtual machines from multiple providers. A flexible protocol is defined to generate real-life instances, and applied on two industrial cases with four real cloud providers. An evolutionary approach, with new specific operators, is introduced and compared to a MIP formulation. Experiments conducted on two data-sets show that the evolutionary approach is viable to tackle real-size instances in reasonable amount of time.","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":"132058467","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.2013.6557790
M. Ware, Nigel Richards
The work presented here investigates the usefulness of Ant Colony Optimisation to solving network schematization problems. This is a well-established problem domain and a number of solutions have appeared in the literature previously. In this paper an Ant Colony System (ACS) based algorithm is presented, together with experimental results and performance analysis. The aim is to provide an algorithm that produces better results and is more efficient (in terms of execution times) than previous solutions. Throughout the paper, ACS is tested and evaluated empirically - that is, experiments are performed and observed, these observations are recorded and subsequently analysed. In order to perform the experiments, a software implementation of the algorithm is constructed and then applied to test data sets. No attempt has been made here to perform a theoretical analysis of ACS. The results presented demonstrate that ACS can be used as an effective means of providing solutions to network schematization problems. In particular, ACS is shown to outperform a previous Simulated Annealing based solution.
{"title":"An Ant Colony System algorithm for automatically schematizing transport network data sets","authors":"M. Ware, Nigel Richards","doi":"10.1109/CEC.2013.6557790","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557790","url":null,"abstract":"The work presented here investigates the usefulness of Ant Colony Optimisation to solving network schematization problems. This is a well-established problem domain and a number of solutions have appeared in the literature previously. In this paper an Ant Colony System (ACS) based algorithm is presented, together with experimental results and performance analysis. The aim is to provide an algorithm that produces better results and is more efficient (in terms of execution times) than previous solutions. Throughout the paper, ACS is tested and evaluated empirically - that is, experiments are performed and observed, these observations are recorded and subsequently analysed. In order to perform the experiments, a software implementation of the algorithm is constructed and then applied to test data sets. No attempt has been made here to perform a theoretical analysis of ACS. The results presented demonstrate that ACS can be used as an effective means of providing solutions to network schematization problems. In particular, ACS is shown to outperform a previous Simulated Annealing based solution.","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":"131715171","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.2013.6557603
Seyednaser Nourashrafeddin, E. Milios, D. Arnold
We propose FSDC, an evolutionary algorithm for Feature Selective Double Clustering of text documents. We first cluster the terms existing in the document corpus. The term clusters are then fed into multiobjective genetic algorithms to prune non-informative terms and form sets of keyterms representing topics. Based on the topic keyterms found, representative documents for each topic are extracted. These documents are then used as seeds to cluster all documents in the dataset. FSDC is compared to some well-known co-clusterers on real text datasets. The experimental results show that our algorithm can outperform the competitors.
{"title":"An evolutionary algorithm for Feature Selective Double Clustering of text documents","authors":"Seyednaser Nourashrafeddin, E. Milios, D. Arnold","doi":"10.1109/CEC.2013.6557603","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557603","url":null,"abstract":"We propose FSDC, an evolutionary algorithm for Feature Selective Double Clustering of text documents. We first cluster the terms existing in the document corpus. The term clusters are then fed into multiobjective genetic algorithms to prune non-informative terms and form sets of keyterms representing topics. Based on the topic keyterms found, representative documents for each topic are extracted. These documents are then used as seeds to cluster all documents in the dataset. FSDC is compared to some well-known co-clusterers on real text datasets. The experimental results show that our algorithm can outperform 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":"125604560","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.2013.6557654
André Assis Lôbo de Oliveira, C. Camilo-Junior, A. Vincenzi
One of the main problems to perform the Software Testing is to find a set of tests (subset from input domain of the problem) which is effective to detect the remaining bugs in the software. The Search-Based Software Testing (SBST) approach uses metaheuristics to find low cost set of tests with a high effectiveness to detect bugs. From several existing test criteria, Mutation Testing is considered quite promising to reveal bugs, despite its high computational cost, due to the great quantity of mutant programs generated. Therefore, this paper addresses the problem of selecting mutant programs and test cases in Mutation Testing context. To this end, it is proposed a Coevolutionary Genetic Algorithm (CGA) and the concept of Genetic Effectiveness, describing a new representation and implementing new genetic operators. The CGA is applied in five benchmarks and the results are compared to other five methods, showing a better performance of the proposed algorithm in subsets automatic selection with better mutation score and greater reduction of computational cost, specifically the amount of testing, when compared with exhaustive test.
{"title":"A coevolutionary algorithm to automatic test case selection and mutant in Mutation Testing","authors":"André Assis Lôbo de Oliveira, C. Camilo-Junior, A. Vincenzi","doi":"10.1109/CEC.2013.6557654","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557654","url":null,"abstract":"One of the main problems to perform the Software Testing is to find a set of tests (subset from input domain of the problem) which is effective to detect the remaining bugs in the software. The Search-Based Software Testing (SBST) approach uses metaheuristics to find low cost set of tests with a high effectiveness to detect bugs. From several existing test criteria, Mutation Testing is considered quite promising to reveal bugs, despite its high computational cost, due to the great quantity of mutant programs generated. Therefore, this paper addresses the problem of selecting mutant programs and test cases in Mutation Testing context. To this end, it is proposed a Coevolutionary Genetic Algorithm (CGA) and the concept of Genetic Effectiveness, describing a new representation and implementing new genetic operators. The CGA is applied in five benchmarks and the results are compared to other five methods, showing a better performance of the proposed algorithm in subsets automatic selection with better mutation score and greater reduction of computational cost, specifically the amount of testing, when compared with exhaustive test.","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":"123255947","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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