Pub Date : 2013-06-20DOI: 10.1109/CEC.2013.6557783
Dung H. Phan, J. Suzuki
This paper proposes and evaluates an evolutionary multiobjective optimization algorithm (EMOA) that eliminates dominance ranking in selection and performs indicator-based selection with the R2 indicator. Although it is known that the R2 indicator possesses desirable properties to quantify the goodness of a solution or a solution set, few attempts have been made until recently to investigate indicator-based EMOAs with the R2 indicator. The proposed EMOA, called R2-IBEA, is designed to obtain a diverse set of Pareto-approximated solutions by correcting an inherent bias in the R2 indicator. (The R2 indicator has a stronger bias to the center of the Pareto front than to its edges.) Experimental results demonstrate that R2IBEA outperforms existing indicator-based, decomposition-based and dominance ranking based EMOAs in the optimality and diversity of solutions. R2-IBEA successfully produces diverse individuals that are distributed weIl in the objective space. It is also empirically verified that R2-IBEA scales weIl from two-dimensional to five-dimensional problems.
{"title":"R2-IBEA: R2 indicator based evolutionary algorithm for multiobjective optimization","authors":"Dung H. Phan, J. Suzuki","doi":"10.1109/CEC.2013.6557783","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557783","url":null,"abstract":"This paper proposes and evaluates an evolutionary multiobjective optimization algorithm (EMOA) that eliminates dominance ranking in selection and performs indicator-based selection with the R2 indicator. Although it is known that the R2 indicator possesses desirable properties to quantify the goodness of a solution or a solution set, few attempts have been made until recently to investigate indicator-based EMOAs with the R2 indicator. The proposed EMOA, called R2-IBEA, is designed to obtain a diverse set of Pareto-approximated solutions by correcting an inherent bias in the R2 indicator. (The R2 indicator has a stronger bias to the center of the Pareto front than to its edges.) Experimental results demonstrate that R2IBEA outperforms existing indicator-based, decomposition-based and dominance ranking based EMOAs in the optimality and diversity of solutions. R2-IBEA successfully produces diverse individuals that are distributed weIl in the objective space. It is also empirically verified that R2-IBEA scales weIl from two-dimensional to five-dimensional 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":"113940453","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.2013.6557672
A. Alanis, N. Arana-Daniel, C. López-Franco, E. Sánchez
This paper deals with adaptive tracking for discrete-time MIMO nonlinear systems in presence of disturbances. A Particle Swarm Optimization (PSO)-Gain selection is used to improve a discrete-time high order sliding mode control law. The paper also includes the respective stability analysis, for the whole system with a strategy. In order to show the applicability of the proposed scheme, simulation results are included for a Van der Pol oscillator.
{"title":"PSO-gain selection to improve a discrete-time second order sliding mode controller","authors":"A. Alanis, N. Arana-Daniel, C. López-Franco, E. Sánchez","doi":"10.1109/CEC.2013.6557672","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557672","url":null,"abstract":"This paper deals with adaptive tracking for discrete-time MIMO nonlinear systems in presence of disturbances. A Particle Swarm Optimization (PSO)-Gain selection is used to improve a discrete-time high order sliding mode control law. The paper also includes the respective stability analysis, for the whole system with a strategy. In order to show the applicability of the proposed scheme, simulation results are included for a Van der Pol oscillator.","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":"113979218","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.2013.6557854
Kalpesh Shelke, S. Jayaraman, Shameek Ghosh, V. Jayaraman
Protein function prediction is an important problem in functional genomics. Typically, protein sequences are represented by feature vectors. A major problem of protein datasets that increase the complexity of classification models is their large number of features. The process of drug discovery often involves the use of quantitative structure-activity relationship (QSAR) models to identify chemical structures that could have good inhibitory effects on specific targets and have low toxicity (non-specific activity). QSAR models are regression or classification models used in the chemical and biological sciences. Because of high dimensionality problems, a feature selection problem is imminent. In this study, we thus employ a hybrid Estimation of Distribution Algorithm (EDA) based filter-wrapper methodology to simultaneously extract informative feature subsets and build robust QSAR models. The performance of the algorithm was tested on the benchmark classification challenge datasets obtained from the CoePRa competition platform, developed in 2006. Our results clearly demonstrate the efficacy of a hybrid EDA filter-wrapper algorithm in comparison to the results reported earlier.
{"title":"Hybrid feature selection and peptide binding affinity prediction using an EDA based algorithm","authors":"Kalpesh Shelke, S. Jayaraman, Shameek Ghosh, V. Jayaraman","doi":"10.1109/CEC.2013.6557854","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557854","url":null,"abstract":"Protein function prediction is an important problem in functional genomics. Typically, protein sequences are represented by feature vectors. A major problem of protein datasets that increase the complexity of classification models is their large number of features. The process of drug discovery often involves the use of quantitative structure-activity relationship (QSAR) models to identify chemical structures that could have good inhibitory effects on specific targets and have low toxicity (non-specific activity). QSAR models are regression or classification models used in the chemical and biological sciences. Because of high dimensionality problems, a feature selection problem is imminent. In this study, we thus employ a hybrid Estimation of Distribution Algorithm (EDA) based filter-wrapper methodology to simultaneously extract informative feature subsets and build robust QSAR models. The performance of the algorithm was tested on the benchmark classification challenge datasets obtained from the CoePRa competition platform, developed in 2006. Our results clearly demonstrate the efficacy of a hybrid EDA filter-wrapper algorithm in comparison to the results reported earlier.","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":"122403275","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.2013.6557907
R. Martins, N. Lourenço, A. Canelas, N. Horta
In the state-of-the-art on analog integrated circuit (IC) automatic routing approaches it is assumed that each terminal has only one port that can be routed, however, in practice a device usually contains multiple electrically-equivalent locations where the connection can be made, multi-port terminals, which are not properly explored. This paper describes an innovative evolutionary approach with multi-port multiterminal (MP/MT) nets for analog IC automatic routing. The netlist and the multi-port terminals are modeled in a Group-Steiner problem that is solved by the Global Router, to obtain the terminal-to-terminal connectivity, and then, for the detailed routing, an optimization kernel is used, namely, an enhanced version of the multi-objective evolutionary algorithm NSGA-II. The Router starts by a single-net procedure, and culminates in a process where all nets are optimized simultaneously. The technology design rules are verified during the evolutionary generation using an in-loop built-in layout evaluation procedure. The automatic routing generation is detailed, and demonstrated for the generation of the layout of a typical analog circuit, for the UMC 130nm design process. The automatically generated layouts are validated using the industrial grade Calibre® tool and the performances of the extracted circuits are compared with the ones achieved in the circuit-level design.
{"title":"Multi-port multi-terminal analog router based on an evolutionary optimization kernel","authors":"R. Martins, N. Lourenço, A. Canelas, N. Horta","doi":"10.1109/CEC.2013.6557907","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557907","url":null,"abstract":"In the state-of-the-art on analog integrated circuit (IC) automatic routing approaches it is assumed that each terminal has only one port that can be routed, however, in practice a device usually contains multiple electrically-equivalent locations where the connection can be made, multi-port terminals, which are not properly explored. This paper describes an innovative evolutionary approach with multi-port multiterminal (MP/MT) nets for analog IC automatic routing. The netlist and the multi-port terminals are modeled in a Group-Steiner problem that is solved by the Global Router, to obtain the terminal-to-terminal connectivity, and then, for the detailed routing, an optimization kernel is used, namely, an enhanced version of the multi-objective evolutionary algorithm NSGA-II. The Router starts by a single-net procedure, and culminates in a process where all nets are optimized simultaneously. The technology design rules are verified during the evolutionary generation using an in-loop built-in layout evaluation procedure. The automatic routing generation is detailed, and demonstrated for the generation of the layout of a typical analog circuit, for the UMC 130nm design process. The automatically generated layouts are validated using the industrial grade Calibre® tool and the performances of the extracted circuits are compared with the ones achieved in the circuit-level design.","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":"122497471","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.2013.6557760
P. Korošec, J. Silc
Continuous ant-colony optimization is an emerging field in numerical optimization, which tries to cope with the optimization challenges arising in modern real-world engineering and scientific domains. One of them is large-scale continuous optimization problem that becomes especially important for the development of recent emerging fields like bio-computing, data mining and production planing. Ant-colony optimization (ACO) is known for its efficiency in solving combinatorial optimization problems. However, its application to real-parameter optimizations appears more challenging, since the pheromone-laying method is not straightforward. In the recent year, there have been developed a several adaptations of the ACO algorithm for continuous optimization. Among them the Continuous Differential Ant-Stigmergy Algorithm (CDASA) arises as promising method for global continuous large-scale optimization. In this paper we address a systematic performance evaluation of CDASA on a predefined test suite and experimental procedure provided for the Competition on Real-Parameter Single Objective Optimization at CEC-2013.
{"title":"The Continuous Differential Ant-Stigmergy Algorithm applied on real-parameter single objective optimization problems","authors":"P. Korošec, J. Silc","doi":"10.1109/CEC.2013.6557760","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557760","url":null,"abstract":"Continuous ant-colony optimization is an emerging field in numerical optimization, which tries to cope with the optimization challenges arising in modern real-world engineering and scientific domains. One of them is large-scale continuous optimization problem that becomes especially important for the development of recent emerging fields like bio-computing, data mining and production planing. Ant-colony optimization (ACO) is known for its efficiency in solving combinatorial optimization problems. However, its application to real-parameter optimizations appears more challenging, since the pheromone-laying method is not straightforward. In the recent year, there have been developed a several adaptations of the ACO algorithm for continuous optimization. Among them the Continuous Differential Ant-Stigmergy Algorithm (CDASA) arises as promising method for global continuous large-scale optimization. In this paper we address a systematic performance evaluation of CDASA on a predefined test suite and experimental procedure provided for the Competition on Real-Parameter Single Objective Optimization at CEC-2013.","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":"131379241","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.2013.6557623
Aniruddha Basak, J. Lohn
Many antenna design and optimization problems require optimizing multimodal, high dimensional, non-convex and inseparable objective functions. This has led researchers towards stochastic optimization techniques such as evolutionary algorithms (EAs) instead of classical gradient-based methods for these applications. However, despite many past successes, very little is known about which types of EAs map best to which types of antenna optimization problems. The goal of this work is to investigate this mapping of EAs to applications by comparing the performance of three EAs on five benchmark antenna design problems and one real-world problem derived from a NASA satellite mission. Performance of these algorithms has been compared on the basis of success rates and average convergence time over 30 independent runs. Our results indicate that age-layered population structure genetic algorithm (ALPS-GA) performed best in terms of success rates and convergence speed. However, on the NASA antenna design problem differential evolution achieved highest success rates, which was marginally better than ALPSGA. We also explored the effect of increasing antenna complexity on the antenna gain.
{"title":"A comparison of evolutionary algorithms on a set of antenna design benchmarks","authors":"Aniruddha Basak, J. Lohn","doi":"10.1109/CEC.2013.6557623","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557623","url":null,"abstract":"Many antenna design and optimization problems require optimizing multimodal, high dimensional, non-convex and inseparable objective functions. This has led researchers towards stochastic optimization techniques such as evolutionary algorithms (EAs) instead of classical gradient-based methods for these applications. However, despite many past successes, very little is known about which types of EAs map best to which types of antenna optimization problems. The goal of this work is to investigate this mapping of EAs to applications by comparing the performance of three EAs on five benchmark antenna design problems and one real-world problem derived from a NASA satellite mission. Performance of these algorithms has been compared on the basis of success rates and average convergence time over 30 independent runs. Our results indicate that age-layered population structure genetic algorithm (ALPS-GA) performed best in terms of success rates and convergence speed. However, on the NASA antenna design problem differential evolution achieved highest success rates, which was marginally better than ALPSGA. We also explored the effect of increasing antenna complexity on the antenna gain.","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":"131553714","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.2013.6557592
Filipe V. Nepomuceno, A. Engelbrecht
Heterogeneous particle swarm optimizers (HPSO) allow particles to use different update equations, referred to as behaviors, within the swarm. Dynamic HPSOs allow the particles to change their behaviors during the search. These HPSOs alter the exploration/exploitation balance during the search which alters the search behavior of the swarm. This paper introduces a new self-adaptive HPSO and compares it with other HPSO algorithms on the CEC 2013 real-parameter optimization benchmark functions. The proposed algorithm keeps track of how successful each behavior has been over a number of iterations and uses that information to select the next behavior of a particle. The results show that the proposed algorithm outperforms existing HPSO algorithms on the benchmark functions.
{"title":"A self-adaptive heterogeneous pso for real-parameter optimization","authors":"Filipe V. Nepomuceno, A. Engelbrecht","doi":"10.1109/CEC.2013.6557592","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557592","url":null,"abstract":"Heterogeneous particle swarm optimizers (HPSO) allow particles to use different update equations, referred to as behaviors, within the swarm. Dynamic HPSOs allow the particles to change their behaviors during the search. These HPSOs alter the exploration/exploitation balance during the search which alters the search behavior of the swarm. This paper introduces a new self-adaptive HPSO and compares it with other HPSO algorithms on the CEC 2013 real-parameter optimization benchmark functions. The proposed algorithm keeps track of how successful each behavior has been over a number of iterations and uses that information to select the next behavior of a particle. The results show that the proposed algorithm outperforms existing HPSO algorithms on the benchmark functions.","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":"121753200","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.2013.6557627
A. Pirisi, F. Grimaccia, M. Mussetta, R. Zich, R. Johnstone, M. Palaniswami, S. Rajasegarar
The sustainable management of coastal and offshore ecosystems, such as for example coral reef environments, requires an energy efficient collection of accurate data across various temporal and spatial scales. To suitably address the energy supply of marine sensors, in this paper a novel energy harvesting device is proposed, based on a Tubular Permanent MagnetLinear Generator (TPM-LiG). The application is related to the sea wave energy conversion for small sensorized buoy. The optimization process is developed by means of evolutionary computation techniques. The advantage of these algorithms is in the wide exploration of the variables space and in the effective exploitation of the fitness function. The algorithm has been tested on a benchmark case and then applied to the optimization of a power-buoy prototype which has been realized in laboratory with potential significant implications in future marine environment applications.
{"title":"Optimization of an energy harvesting buoy for coral reef monitoring","authors":"A. Pirisi, F. Grimaccia, M. Mussetta, R. Zich, R. Johnstone, M. Palaniswami, S. Rajasegarar","doi":"10.1109/CEC.2013.6557627","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557627","url":null,"abstract":"The sustainable management of coastal and offshore ecosystems, such as for example coral reef environments, requires an energy efficient collection of accurate data across various temporal and spatial scales. To suitably address the energy supply of marine sensors, in this paper a novel energy harvesting device is proposed, based on a Tubular Permanent MagnetLinear Generator (TPM-LiG). The application is related to the sea wave energy conversion for small sensorized buoy. The optimization process is developed by means of evolutionary computation techniques. The advantage of these algorithms is in the wide exploration of the variables space and in the effective exploitation of the fitness function. The algorithm has been tested on a benchmark case and then applied to the optimization of a power-buoy prototype which has been realized in laboratory with potential significant implications in future marine environment 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":"121780341","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.2013.6557568
Alan Díaz-Manríquez, G. T. Pulido, R. Becerra
In recent years, the development of hybrid approaches to solve multiobjective optimization problems has become an important trend in the evolutionary computation community. Despite hybrid approaches of mathematical programming techniques with multiobjective evolutionary algorithms are not very popular, when both fields are successfully coupled, results are impressive. However, the main objective of this sort of hybridization relays on the needing of several executions of the mathematical approach in order to obtain a sample of the Pareto front, raising with this, the number of fitness function evaluations. However, the use of surrogate models has become a recurrent approach to diminish the number of function evaluations. In this work, a hybrid operator that transforms the original multiobjective problem into a set of modified goal programming models is proposed. Furthermore, a local surrogate model is used instead of the real function in the hybrid operator. The goal programming model with the surrogate is optimized by a direct search method. Additionally, a standalone algorithm that uses the hybrid operator is here proposed. The new algorithm is validated using several test problems and performance measures commonly adopted in the specialized literature. Results indicate that the proposed operator gives rise to an effective algorithm, which produces results that are competitive with respect to those obtained by two well-known multiobjective evolutionary algorithms.
{"title":"A hybrid local search operator for multiobjective optimization","authors":"Alan Díaz-Manríquez, G. T. Pulido, R. Becerra","doi":"10.1109/CEC.2013.6557568","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557568","url":null,"abstract":"In recent years, the development of hybrid approaches to solve multiobjective optimization problems has become an important trend in the evolutionary computation community. Despite hybrid approaches of mathematical programming techniques with multiobjective evolutionary algorithms are not very popular, when both fields are successfully coupled, results are impressive. However, the main objective of this sort of hybridization relays on the needing of several executions of the mathematical approach in order to obtain a sample of the Pareto front, raising with this, the number of fitness function evaluations. However, the use of surrogate models has become a recurrent approach to diminish the number of function evaluations. In this work, a hybrid operator that transforms the original multiobjective problem into a set of modified goal programming models is proposed. Furthermore, a local surrogate model is used instead of the real function in the hybrid operator. The goal programming model with the surrogate is optimized by a direct search method. Additionally, a standalone algorithm that uses the hybrid operator is here proposed. The new algorithm is validated using several test problems and performance measures commonly adopted in the specialized literature. Results indicate that the proposed operator gives rise to an effective algorithm, which produces results that are competitive with respect to those obtained by two well-known multiobjective evolutionary algorithms.","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":"127769412","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.2013.6557593
I. Loshchilov
This paper investigates the performance of 6 versions of Covariance Matrix Adaptation Evolution Strategy (CMAES) with restarts on a set of 28 noiseless optimization problems (including 23 multi-modal ones) designed for the special session on real-parameter optimization of CEC 2013. The experimental validation of the restart strategies shows that: i). the versions of CMA-ES with weighted active covariance matrix update outperform the original versions of CMA-ES, especially on ill-conditioned problems; ii). the original restart strategies with increasing population size (IPOP) are usually outperformed by the bi-population restart strategies where the initial mutation stepsize is also varied; iii). the recently proposed alternative restart strategies for CMA-ES demonstrate a competitive performance and are ranked first w.r.t. the proportion of function-target pairs solved after the full run on all 10-, 30- and 50-dimensional problems.
{"title":"CMA-ES with restarts for solving CEC 2013 benchmark problems","authors":"I. Loshchilov","doi":"10.1109/CEC.2013.6557593","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557593","url":null,"abstract":"This paper investigates the performance of 6 versions of Covariance Matrix Adaptation Evolution Strategy (CMAES) with restarts on a set of 28 noiseless optimization problems (including 23 multi-modal ones) designed for the special session on real-parameter optimization of CEC 2013. The experimental validation of the restart strategies shows that: i). the versions of CMA-ES with weighted active covariance matrix update outperform the original versions of CMA-ES, especially on ill-conditioned problems; ii). the original restart strategies with increasing population size (IPOP) are usually outperformed by the bi-population restart strategies where the initial mutation stepsize is also varied; iii). the recently proposed alternative restart strategies for CMA-ES demonstrate a competitive performance and are ranked first w.r.t. the proportion of function-target pairs solved after the full run on all 10-, 30- and 50-dimensional 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":"133959735","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：