Pub Date : 2018-07-01DOI: 10.1109/CEC.2018.8477914
Kenya Jinno
We propose a novel optimization algorithm which named Nonlinear Map-model Optimization (abbr. NMO) method. The NMO is classified as swarm intelligence (abbr. SI) optimizer and consists of some search individuals whose dynamics is driven by a simple nonlinear map. The search point distribution is controlled by the simple nonlinear map. Based on the theoretical analysis results about the dynamics of the particle swarm optimization, we set so that the searching point distribution of the NMO becomes an optimal distribution. Also, the simple nonlinear map generates a chaotic search point time series while keeping the search range. Such a time series can efficiently search within the search range. As a result, NMO can search along the valley of the evaluation function. Namely, NMO is considered to have a rotation invariance and a scaling invariance. In general, the computation amount of SI optimizer is proportional to the number of search elements included in the SI optimizer. However, the NMO requires only a few particles comparing with other swarm intelligence optimizers. Therefore, the computation amount is the smaller than the other methods. As the result, the search performance of the NMO exhibits better than Standard PSO 2011.
{"title":"Nonlinear Map Optimization","authors":"Kenya Jinno","doi":"10.1109/CEC.2018.8477914","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477914","url":null,"abstract":"We propose a novel optimization algorithm which named Nonlinear Map-model Optimization (abbr. NMO) method. The NMO is classified as swarm intelligence (abbr. SI) optimizer and consists of some search individuals whose dynamics is driven by a simple nonlinear map. The search point distribution is controlled by the simple nonlinear map. Based on the theoretical analysis results about the dynamics of the particle swarm optimization, we set so that the searching point distribution of the NMO becomes an optimal distribution. Also, the simple nonlinear map generates a chaotic search point time series while keeping the search range. Such a time series can efficiently search within the search range. As a result, NMO can search along the valley of the evaluation function. Namely, NMO is considered to have a rotation invariance and a scaling invariance. In general, the computation amount of SI optimizer is proportional to the number of search elements included in the SI optimizer. However, the NMO requires only a few particles comparing with other swarm intelligence optimizers. Therefore, the computation amount is the smaller than the other methods. As the result, the search performance of the NMO exhibits better than Standard PSO 2011.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125405537","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 : 2018-07-01DOI: 10.1109/CEC.2018.8477946
Ali Hebbal, Loïc Brevault, M. Balesdent, E. Talbi, N. Melab
Efficient Global Optimization (EGO) is widely used for the optimization of computationally expensive black-box functions. It uses a surrogate modeling technique based on Gaussian Processes (Kriging). However, due to the use of a stationary covariance, Kriging is not well suited for approximating non stationary functions. This paper explores the integration of Deep Gaussian processes (DGP) in EGO framework to deal with the non-stationary issues and investigates the induced challenges and opportunities. Numerical experimentations are performed on analytical problems to highlight the different aspects of DGP and EGO.
{"title":"Efficient Global Optimization Using Deep Gaussian Processes","authors":"Ali Hebbal, Loïc Brevault, M. Balesdent, E. Talbi, N. Melab","doi":"10.1109/CEC.2018.8477946","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477946","url":null,"abstract":"Efficient Global Optimization (EGO) is widely used for the optimization of computationally expensive black-box functions. It uses a surrogate modeling technique based on Gaussian Processes (Kriging). However, due to the use of a stationary covariance, Kriging is not well suited for approximating non stationary functions. This paper explores the integration of Deep Gaussian processes (DGP) in EGO framework to deal with the non-stationary issues and investigates the induced challenges and opportunities. Numerical experimentations are performed on analytical problems to highlight the different aspects of DGP and EGO.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126730328","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 : 2018-07-01DOI: 10.1109/CEC.2018.8477669
Samuel E. L. Oliveira, Victor Diniz, A. Lacerda, G. Pappa
Recommender systems help users to overcome the information overload problem by selecting relevant items according to their preferences. This paper deals with the problem of rank aggregation in recommender systems, where we want to generate a single consensus ranking from a given set of input rankings generated by different recommendation algorithms. This problem is NP-hard, and hence the use of meta-heuristics to solve it is appealing. Although accurate suggestions are mandatory for effective recommender systems, other recommendation quality measures need to be taken into account for delivering high-quality suggestions. This paper proposes Multi-objective Evolutionary Rank Aggregation (MERA), a genetic programming algorithm following the concepts of SPEA2 that considers three measures when suggesting items to users, namely mean average precision, diversity, and novelty. The method was tested in 3 realworld recommendation datasets, and the results show MERA can indeed find a balance for these metrics while generating a diverse set of solutions to the problem. MERA was able to return solutions with improvements of up to 15% in diversity (for the Movielens 1M dataset) and 7% in novelty (for the Filmtrust dataset) while maintaining, or even improving, the values of precision.
{"title":"Multi-objective Evolutionary Rank Aggregation for Recommender Systems","authors":"Samuel E. L. Oliveira, Victor Diniz, A. Lacerda, G. Pappa","doi":"10.1109/CEC.2018.8477669","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477669","url":null,"abstract":"Recommender systems help users to overcome the information overload problem by selecting relevant items according to their preferences. This paper deals with the problem of rank aggregation in recommender systems, where we want to generate a single consensus ranking from a given set of input rankings generated by different recommendation algorithms. This problem is NP-hard, and hence the use of meta-heuristics to solve it is appealing. Although accurate suggestions are mandatory for effective recommender systems, other recommendation quality measures need to be taken into account for delivering high-quality suggestions. This paper proposes Multi-objective Evolutionary Rank Aggregation (MERA), a genetic programming algorithm following the concepts of SPEA2 that considers three measures when suggesting items to users, namely mean average precision, diversity, and novelty. The method was tested in 3 realworld recommendation datasets, and the results show MERA can indeed find a balance for these metrics while generating a diverse set of solutions to the problem. MERA was able to return solutions with improvements of up to 15% in diversity (for the Movielens 1M dataset) and 7% in novelty (for the Filmtrust dataset) while maintaining, or even improving, the values of precision.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126866686","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 : 2018-07-01DOI: 10.1109/CEC.2018.8477722
Yongliang Chen, J. Zhong, Mingkui Tan
Multi-tasking optimization has now become a promising research topic that has attracted increasing attention from researchers. In this paper, an efficient memetic evolutionary multi-tasking optimization framework is proposed. The key idea is to use multiple subpopulations to solve multiple tasks, with each subpopulation focusing on solving a single task. A knowledge transferring crossover is proposed to transfer knowledge between subpopulations during the evolution. The proposed framework is further integrated with a multi-objective differential evolution and an adaptive local search strategy, forming a memetic multiobjective DE named MM-DE for multi-tasking optimization. The proposed MM-DE is compared with the state-of-the-art multi-tasking multi-objective evolutionary algorithm (named MO-MFEA) on nine benchmark problems in the CEC 2017 multitasking optimization competition. The experimental results have demonstrated that the proposed MM-DE can offer very promising performance.
{"title":"A Fast Memetic Multi-Objective Differential Evolution for Multi-Tasking Optimization","authors":"Yongliang Chen, J. Zhong, Mingkui Tan","doi":"10.1109/CEC.2018.8477722","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477722","url":null,"abstract":"Multi-tasking optimization has now become a promising research topic that has attracted increasing attention from researchers. In this paper, an efficient memetic evolutionary multi-tasking optimization framework is proposed. The key idea is to use multiple subpopulations to solve multiple tasks, with each subpopulation focusing on solving a single task. A knowledge transferring crossover is proposed to transfer knowledge between subpopulations during the evolution. The proposed framework is further integrated with a multi-objective differential evolution and an adaptive local search strategy, forming a memetic multiobjective DE named MM-DE for multi-tasking optimization. The proposed MM-DE is compared with the state-of-the-art multi-tasking multi-objective evolutionary algorithm (named MO-MFEA) on nine benchmark problems in the CEC 2017 multitasking optimization competition. The experimental results have demonstrated that the proposed MM-DE can offer very promising performance.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114938454","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 : 2018-07-01DOI: 10.1109/CEC.2018.8477848
M. M. Hussain, N. Fujimoto
In last couple of years, parallel two or many objective MOPSO (Multi-objective Particle Swarm Optimization) have been proposed in literature. Denumerable implementations were published, however they had not achieved faster execution time and good Pareto fronts. They have alluded some limitation of archive handling, picked up nondominated solutions, high dimensional problems and so on for large swarm population. Moreover, none of the researchers have implemented MOPSO and tested the performance for large swarm population and high dimensional problem simultaneously. In particular, they skipped high dimensional problems. This paper presents a faster implementation of parallel MOPSO on a GPU based on the CUDA architecture, which uses coalescing memory access, pseudorandom number generator (PRNG), Thrust library, atomic function, parallel archiving and so on. In addition, our implementation has a positive impact on the performance to solve high dimensional optimization problems with large swarm population. Therefore, our proposed algorithm can be widely used in real optimizing problems. The proposed parallel implementation of MOPSO using a master-slave model provides up to 182 times speedup compared to the corresponding CPU MOPSO.
{"title":"Parallel Multi-Objective Particle Swarm Optimization for Large Swarm and High Dimensional Problems","authors":"M. M. Hussain, N. Fujimoto","doi":"10.1109/CEC.2018.8477848","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477848","url":null,"abstract":"In last couple of years, parallel two or many objective MOPSO (Multi-objective Particle Swarm Optimization) have been proposed in literature. Denumerable implementations were published, however they had not achieved faster execution time and good Pareto fronts. They have alluded some limitation of archive handling, picked up nondominated solutions, high dimensional problems and so on for large swarm population. Moreover, none of the researchers have implemented MOPSO and tested the performance for large swarm population and high dimensional problem simultaneously. In particular, they skipped high dimensional problems. This paper presents a faster implementation of parallel MOPSO on a GPU based on the CUDA architecture, which uses coalescing memory access, pseudorandom number generator (PRNG), Thrust library, atomic function, parallel archiving and so on. In addition, our implementation has a positive impact on the performance to solve high dimensional optimization problems with large swarm population. Therefore, our proposed algorithm can be widely used in real optimizing problems. The proposed parallel implementation of MOPSO using a master-slave model provides up to 182 times speedup compared to the corresponding CPU MOPSO.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115461339","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 : 2018-07-01DOI: 10.1109/CEC.2018.8477737
M. Arzamendia, Daniel Gutiérrez-Reina, S. T. Marín, D. Gregor, H. Tawfik
An evolutionary-based path planning is designed for an Autonomous Surface Vehicle (ASV) used in environmental monitoring tasks. The main objective is that the ASV covers the maximum area of a mass of water like the Ypacarai Lake while taking water samples for sensing pollution conditions. Such coverage problem is transformed into a path planning optimization problem through the placement of a set of data beacons located at the shore of the lake and considering the relationship between the distance travelled by the ASV and the area of the lake covered. The optimal set of beacons to be visited by the ASV has been modeled through Eulerian circuits. Due to the complexity of the optimization problem, a metaheuristic technique like a Genetic Algorithm (GA) is used to obtain quasi-optimal solutions in both models. The parameters of the GA are tuned and then the obtained Eulerian Circuit is compared with a lawnmower and a random approaches obtaining an improvement of up to the double of the lake.
{"title":"Evolutionary Computation for Solving Path Planning of an Autonomous Surface Vehicle Using Eulerian Graphs","authors":"M. Arzamendia, Daniel Gutiérrez-Reina, S. T. Marín, D. Gregor, H. Tawfik","doi":"10.1109/CEC.2018.8477737","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477737","url":null,"abstract":"An evolutionary-based path planning is designed for an Autonomous Surface Vehicle (ASV) used in environmental monitoring tasks. The main objective is that the ASV covers the maximum area of a mass of water like the Ypacarai Lake while taking water samples for sensing pollution conditions. Such coverage problem is transformed into a path planning optimization problem through the placement of a set of data beacons located at the shore of the lake and considering the relationship between the distance travelled by the ASV and the area of the lake covered. The optimal set of beacons to be visited by the ASV has been modeled through Eulerian circuits. Due to the complexity of the optimization problem, a metaheuristic technique like a Genetic Algorithm (GA) is used to obtain quasi-optimal solutions in both models. The parameters of the GA are tuned and then the obtained Eulerian Circuit is compared with a lawnmower and a random approaches obtaining an improvement of up to the double of the lake.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122490591","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 : 2018-07-01DOI: 10.1109/CEC.2018.8477786
Bryan Hoke, Dean Frederick Hougen
The ability to learn makes intelligent systems more adaptive. One approach to the development of learning algorithms is to evolve them using evolutionary algorithms. The evolution of learning is interesting as a practical matter because harnessing it may allow us to develop better artificial intelligence; it is also interesting from a theoretical perspective of understanding how the sophisticated learning seen in nature could have arisen. A potential obstacle to the evolution of learning when alternative behavioral strategies (e.g., instincts) can evolve is that learning individuals tend to exhibit ineffective behavior before effective behavior is learned. Nurturing, defined as one individual investing in the development of another individual with which it has an ongoing relationship, is often seen in nature in species that exhibit sophisticated learning behavior. It is hypothesized that nurturing may be able to increase the competitiveness of learning in an evolutionary environment by ameliorating the consequences of incorrect initial behavior. Here we expand upon a foundational work in the evolution of learning to also enable the evolution of instincts and then examine the strategies evolved with and without a nurturing condition in which individuals are not penalized for mistakes made during a learning period. It is found that nurturing promotes the evolution of generalized supervised learning in these environments.
{"title":"Nurturing Promotes the Evolution of Generalized Supervised Learning","authors":"Bryan Hoke, Dean Frederick Hougen","doi":"10.1109/CEC.2018.8477786","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477786","url":null,"abstract":"The ability to learn makes intelligent systems more adaptive. One approach to the development of learning algorithms is to evolve them using evolutionary algorithms. The evolution of learning is interesting as a practical matter because harnessing it may allow us to develop better artificial intelligence; it is also interesting from a theoretical perspective of understanding how the sophisticated learning seen in nature could have arisen. A potential obstacle to the evolution of learning when alternative behavioral strategies (e.g., instincts) can evolve is that learning individuals tend to exhibit ineffective behavior before effective behavior is learned. Nurturing, defined as one individual investing in the development of another individual with which it has an ongoing relationship, is often seen in nature in species that exhibit sophisticated learning behavior. It is hypothesized that nurturing may be able to increase the competitiveness of learning in an evolutionary environment by ameliorating the consequences of incorrect initial behavior. Here we expand upon a foundational work in the evolution of learning to also enable the evolution of instincts and then examine the strategies evolved with and without a nurturing condition in which individuals are not penalized for mistakes made during a learning period. It is found that nurturing promotes the evolution of generalized supervised learning in these environments.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"366 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122652405","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 : 2018-07-01DOI: 10.1109/CEC.2018.8477747
Nicolás Rojas-Morales, M. Riff, B. Neveu
A Constraint Satisfaction Problem is composed by a set of variables, their related domains and a set of constraints among the variables that must be satisfied. These are known as hard problems to be solved. Many algorithms have been proposed to solve these problems. Metaheuristics and in particular ant-based algorithms have been used to solve difficult instances. In this paper, we propose new heuristics to be included in an ant-based algorithm in order to improve its performance when tackling hard constraint satisfaction problems. These heuristics are focused on the availability of consistent variable values and to restrict the ants collaborative information to the feasibility. To evaluate these heuristics we used the well-known Ant Solver algorithm and tested with problem instances from the transition phase. Results show that using our heuristics the Ants algorithm increases the number of problems that it is able to solve. Finally, a statistical analysis is presented to compare these approaches.
{"title":"Feasibility and Availability Based Heuristics for ACO Algorithms Solving Binary CSP","authors":"Nicolás Rojas-Morales, M. Riff, B. Neveu","doi":"10.1109/CEC.2018.8477747","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477747","url":null,"abstract":"A Constraint Satisfaction Problem is composed by a set of variables, their related domains and a set of constraints among the variables that must be satisfied. These are known as hard problems to be solved. Many algorithms have been proposed to solve these problems. Metaheuristics and in particular ant-based algorithms have been used to solve difficult instances. In this paper, we propose new heuristics to be included in an ant-based algorithm in order to improve its performance when tackling hard constraint satisfaction problems. These heuristics are focused on the availability of consistent variable values and to restrict the ants collaborative information to the feasibility. To evaluate these heuristics we used the well-known Ant Solver algorithm and tested with problem instances from the transition phase. Results show that using our heuristics the Ants algorithm increases the number of problems that it is able to solve. Finally, a statistical analysis is presented to compare these approaches.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122975833","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 : 2018-07-01DOI: 10.1109/CEC.2018.8477930
Richard A. Gonçalves, C. Almeida, R. Lüders, M. Delgado
Hyper-Heuristics are high-level methodologies which select or generate heuristics. Despite their success, there are only few hyper-heuristics developed for many-objective optimization. Our approach, namely MOEA/D-LinUCB, combines the MOEA/D framework with a new selection hyper-heuristic to solve many-objective problems. It uses an innovative Contextual Multi-Armed Bandit (MAB) to determine the low level heuristic (Differential Evolution mutation strategy) that should be applied to each individual during MOEA/D execution. The main advantage of using Contextual MAB is to include information about the current search state into the selection procedure. We tested MOEA/D-LinUCB on a well established set of 9 instances from the WFG benchmark for a number of objectives varying from 3 to 20. The IGD indicator and Kruskal-Wallis and Dunn-Sidak's statistical tests are applied to evaluate the algorithm performance. Four variants of the proposed algorithm are compared with each other to define a proper configuration. A properly configured MOEA/D-LinUCB is then compared with MOEA/D-FRRMAB and MOEAID-DRA-two well-known MOEA/D-based algorithms. Results show that MOEA/D-LinUCB performs well, particularly when the number of objectives is 10 or greater. Therefore, MOEA/D-LinUCB can be considered as a promising many-objective Hyper-Heuristic.
{"title":"A New Hyper-Heuristic Based on a Contextual Multi-Armed Bandit for Many-Objective Optimization","authors":"Richard A. Gonçalves, C. Almeida, R. Lüders, M. Delgado","doi":"10.1109/CEC.2018.8477930","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477930","url":null,"abstract":"Hyper-Heuristics are high-level methodologies which select or generate heuristics. Despite their success, there are only few hyper-heuristics developed for many-objective optimization. Our approach, namely MOEA/D-LinUCB, combines the MOEA/D framework with a new selection hyper-heuristic to solve many-objective problems. It uses an innovative Contextual Multi-Armed Bandit (MAB) to determine the low level heuristic (Differential Evolution mutation strategy) that should be applied to each individual during MOEA/D execution. The main advantage of using Contextual MAB is to include information about the current search state into the selection procedure. We tested MOEA/D-LinUCB on a well established set of 9 instances from the WFG benchmark for a number of objectives varying from 3 to 20. The IGD indicator and Kruskal-Wallis and Dunn-Sidak's statistical tests are applied to evaluate the algorithm performance. Four variants of the proposed algorithm are compared with each other to define a proper configuration. A properly configured MOEA/D-LinUCB is then compared with MOEA/D-FRRMAB and MOEAID-DRA-two well-known MOEA/D-based algorithms. Results show that MOEA/D-LinUCB performs well, particularly when the number of objectives is 10 or greater. Therefore, MOEA/D-LinUCB can be considered as a promising many-objective Hyper-Heuristic.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122999932","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 : 2018-07-01DOI: 10.1109/CEC.2018.8477767
L. G. Fonseca, P. Capriles, G. R. Duarte
The basic principle of the building energy efficiency is to use less energy for operations such as heating, cooling, lighting and other appliances, without impacting the health and comfort of its occupants. In order to measure energy efficiency in a building, it is necessary to estimate its heating and cooling loads, considering some of its physical characteristics such as geometry, material properties as well as local weather conditions, project costs and environmental impact. Machine Learning Methods can be applied to solve this problem by estimating a response from a set of inputs. This paper evaluates the performance of Gaussian Processes, also known as kriging, for predicting cooling and heating loads of residential buildings. The dataset consists of 768 samples with eight input variables and two output variables derived from building designs. The parameters were selected based on exhaustive search with cross validation. Four statistical measures and one synthesis index were used for the performance assessment and comparison. The results show Gaussian Processes consistently outperform other machine learning techniques such as Neural Networks, Support Vector Machines and Random Forests. The proposed framework resulted in accurate prediction models contributing to savings in the initial phase of the project avoidlng the modeling and testing of several designs.
{"title":"Modeling Heating and Cooling Loads in Buildings Using Gaussian Processes","authors":"L. G. Fonseca, P. Capriles, G. R. Duarte","doi":"10.1109/CEC.2018.8477767","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477767","url":null,"abstract":"The basic principle of the building energy efficiency is to use less energy for operations such as heating, cooling, lighting and other appliances, without impacting the health and comfort of its occupants. In order to measure energy efficiency in a building, it is necessary to estimate its heating and cooling loads, considering some of its physical characteristics such as geometry, material properties as well as local weather conditions, project costs and environmental impact. Machine Learning Methods can be applied to solve this problem by estimating a response from a set of inputs. This paper evaluates the performance of Gaussian Processes, also known as kriging, for predicting cooling and heating loads of residential buildings. The dataset consists of 768 samples with eight input variables and two output variables derived from building designs. The parameters were selected based on exhaustive search with cross validation. Four statistical measures and one synthesis index were used for the performance assessment and comparison. The results show Gaussian Processes consistently outperform other machine learning techniques such as Neural Networks, Support Vector Machines and Random Forests. The proposed framework resulted in accurate prediction models contributing to savings in the initial phase of the project avoidlng the modeling and testing of several designs.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122073388","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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