Pub Date : 2018-07-01DOI: 10.1109/CEC.2018.8477939
Feiyu Zhang, Yuning Chen, Y. Chen
Agile Earth Observing Satellite (AEOS) scheduling problem (AEOSSP) consists in selecting a subset of tasks from a given task set which are then scheduled on the agile satellite with the purpose of maximizing the total reward of scheduled tasks. AEOSSP is strongly NP-hard and therefore existing solution approaches mainly fall in the field of heuristics and metaheuristics. According to the no free lunch theory, it is impossible to find a single heuristic that is well-applied to any problem instance and a problem-tailored heuristic is always needed. In this paper, we propose a genetic programming based evolutionary approach (GPEA) to automatically evolve a best-suited constructive heuristic for any given AEOSSP instance. The programs (individuals) of GPEA are heuristic rules encoded as trees of mathematical functions. The fitness of the program is evaluated through mapping the mathematical function to an AEOSSP solution using a timeline-based construction algorithm. Computational results on a set of well-designed AEOSSP scenarios show that the proposed GPEA leads to a heuristic algorithm that outperforms recently published sophisticated meta-heuristic algorithm (ALNS). Additional experiments were carried out to demonstrate that the timeline based construction algorithm plays a significant role in matching time-related characteristics in comparison to four commonly used heuristic algorithms. Our results also showed that the evolved heuristic rules preserve a certain extent of generality.
{"title":"Evolving Constructive Heuristics for Agile Earth Observing Satellite Scheduling Problem with Genetic Programming","authors":"Feiyu Zhang, Yuning Chen, Y. Chen","doi":"10.1109/CEC.2018.8477939","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477939","url":null,"abstract":"Agile Earth Observing Satellite (AEOS) scheduling problem (AEOSSP) consists in selecting a subset of tasks from a given task set which are then scheduled on the agile satellite with the purpose of maximizing the total reward of scheduled tasks. AEOSSP is strongly NP-hard and therefore existing solution approaches mainly fall in the field of heuristics and metaheuristics. According to the no free lunch theory, it is impossible to find a single heuristic that is well-applied to any problem instance and a problem-tailored heuristic is always needed. In this paper, we propose a genetic programming based evolutionary approach (GPEA) to automatically evolve a best-suited constructive heuristic for any given AEOSSP instance. The programs (individuals) of GPEA are heuristic rules encoded as trees of mathematical functions. The fitness of the program is evaluated through mapping the mathematical function to an AEOSSP solution using a timeline-based construction algorithm. Computational results on a set of well-designed AEOSSP scenarios show that the proposed GPEA leads to a heuristic algorithm that outperforms recently published sophisticated meta-heuristic algorithm (ALNS). Additional experiments were carried out to demonstrate that the timeline based construction algorithm plays a significant role in matching time-related characteristics in comparison to four commonly used heuristic algorithms. Our results also showed that the evolved heuristic rules preserve a certain extent of generality.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"57 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":"131439943","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.2018.8477896
Glauber Botelho, L. Bezerra, André Britto, Leila Silva
Refactoring is a modification in the internal structure of software, in order to improve quality, understandability and maintainability, without changing its observable behavior. Search Based Software Refactoring (SBSR) deals with automatic software refactoring processes using optimization algorithms. In this context, here we investigate the problem of finding a sequence of refactorings that provides code improvement, according to software quality attributes, expressed by a combination of software metrics. There are multiple criteria to define the quality of a solution, therefore this problem is defined as a Many-Objective Combinatorial Optimization Problem. There is a lack of works that focus on Many-Objective Discrete Problems in SBSR. In this direction, this work proposes a Many-Objective Estimation Distributed Algorithm to find a sequence of refactorings on an object-oriented software. The algorithm explores archiving methods and probabilistic models. A set of experiments is performed, with the aim of investigating which is the best algorithm configuration, regarding the probabilistic model and selection procedure.
{"title":"A Many-Objective Estimation Distributed Algorithm Applied to Search Based Software Refactoring","authors":"Glauber Botelho, L. Bezerra, André Britto, Leila Silva","doi":"10.1109/CEC.2018.8477896","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477896","url":null,"abstract":"Refactoring is a modification in the internal structure of software, in order to improve quality, understandability and maintainability, without changing its observable behavior. Search Based Software Refactoring (SBSR) deals with automatic software refactoring processes using optimization algorithms. In this context, here we investigate the problem of finding a sequence of refactorings that provides code improvement, according to software quality attributes, expressed by a combination of software metrics. There are multiple criteria to define the quality of a solution, therefore this problem is defined as a Many-Objective Combinatorial Optimization Problem. There is a lack of works that focus on Many-Objective Discrete Problems in SBSR. In this direction, this work proposes a Many-Objective Estimation Distributed Algorithm to find a sequence of refactorings on an object-oriented software. The algorithm explores archiving methods and probabilistic models. A set of experiments is performed, with the aim of investigating which is the best algorithm configuration, regarding the probabilistic model and selection procedure.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"13 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":"115589050","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.2018.8477932
Noe Elisa, Longzhi Yang, N. Naik
Intrusion detection systems are developed with the abilities to discriminate between normal and anomalous traffic behaviours. The core challenge in implementing an intrusion detection systems is to determine and stop anomalous traffic behavior precisely before it causes any adverse effects to the network, information systems, or any other hardware and digital assets which forming or in the cyberspace. Inspired by the biological immune system, Dendritic Cell Algorithm (DCA) is a classification algorithm developed for the purpose of anomaly detection based on the danger theory and the functioning of human immune dendritic cells. In its core operation, DCA uses a weighted sum function to derive the output cumulative values from the input signals. The weights used in this function are either derived empirically from the data or defined by users. Due to this, the algorithm opens the doors for users to specify the weights that may not produce optimal result (often accuracy). This paper proposes a weight optimisation approach implemented using the popular stochastic search tool, genetic algorithm. The approach is validated and evaluated using the KDD99 dataset with promising results generated.
{"title":"Dendritic Cell Algorithm with Optimised Parameters Using Genetic Algorithm","authors":"Noe Elisa, Longzhi Yang, N. Naik","doi":"10.1109/CEC.2018.8477932","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477932","url":null,"abstract":"Intrusion detection systems are developed with the abilities to discriminate between normal and anomalous traffic behaviours. The core challenge in implementing an intrusion detection systems is to determine and stop anomalous traffic behavior precisely before it causes any adverse effects to the network, information systems, or any other hardware and digital assets which forming or in the cyberspace. Inspired by the biological immune system, Dendritic Cell Algorithm (DCA) is a classification algorithm developed for the purpose of anomaly detection based on the danger theory and the functioning of human immune dendritic cells. In its core operation, DCA uses a weighted sum function to derive the output cumulative values from the input signals. The weights used in this function are either derived empirically from the data or defined by users. Due to this, the algorithm opens the doors for users to specify the weights that may not produce optimal result (often accuracy). This paper proposes a weight optimisation approach implemented using the popular stochastic search tool, genetic algorithm. The approach is validated and evaluated using the KDD99 dataset with promising results generated.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"32 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":"115620612","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.2018.8477791
Jacob Nyman, Kazi Shah Nawaz Ripon
This paper presents a comparative survey on metaheuristics applied to the multiobjective surgery admission planning problem. Three well-known metaheuristics: genetic algorithm (GA), simulated annealing (SA) and variable neighbourhood descent (VND) are compared using the Wilcoxon signed rank test. The metaheuristics are also benchmarked against a hybrid GA that uses the VND as a local search procedure. The weighted sum method is used to balance five competing objectives: operating room overtime, operating room idle time, surgeon overtime, surgeon idle time and patient waiting time. As a preparation for future multiobjectivity analysis, a simple example shows how several non-dominated trade-off solutions may be presented to the decision maker using the $epsilon$-constrained method and the non-dominated sorting genetic algorithm II (NSGA-II). The results are meant to serve as a starting point for further development and testing where the challenge of uncertain surgery durations will be included.
{"title":"Metaheuristics for the Multiobjective Surgery Admission Planning Problem","authors":"Jacob Nyman, Kazi Shah Nawaz Ripon","doi":"10.1109/CEC.2018.8477791","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477791","url":null,"abstract":"This paper presents a comparative survey on metaheuristics applied to the multiobjective surgery admission planning problem. Three well-known metaheuristics: genetic algorithm (GA), simulated annealing (SA) and variable neighbourhood descent (VND) are compared using the Wilcoxon signed rank test. The metaheuristics are also benchmarked against a hybrid GA that uses the VND as a local search procedure. The weighted sum method is used to balance five competing objectives: operating room overtime, operating room idle time, surgeon overtime, surgeon idle time and patient waiting time. As a preparation for future multiobjectivity analysis, a simple example shows how several non-dominated trade-off solutions may be presented to the decision maker using the $epsilon$-constrained method and the non-dominated sorting genetic algorithm II (NSGA-II). The results are meant to serve as a starting point for further development and testing where the challenge of uncertain surgery durations will be included.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"362 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":"115939837","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.2018.8477941
Zixu Liu, Xiao-Jun Zeng, Zhihua Yang
The penetration of renewable resources in the wholesale electricity market and the demand response in the retail market cause the demand and the supply to become more unpredictable. The ISO is hard to efficiently schedule the production and dispatch the demand. Furthermore, strategic bidding in a more competitive environment is an important problem for the generator. Forecasting the hourly market clearing price (MCP) in the day-ahead electricity market is one of essential task for any bidding decision making. But only a single predicted value of MCP cannot offer enough help for the generator to select the optimal bidding strategies. Aiming at challenge these tasks, we design a new wholesale mechanism in which the ISO declares an interval demand to the wholesale market. The interval demand is more robust than a single demand figure and enables the ISO to handle unpredictable demand under the DR programs. We also developed a forecasting model to forecast a MCP function under the interval demand and introduce the notion of confidence interval to the forecasting model. The confidence interval predicts the exact range of hourly MCP. Based on these work, the optimal bidding strategies for the generator under an interval demand is also illustrated.
{"title":"Demand Based Bidding Strategies Under Interval Demand for Integrated Demand and Supply Management","authors":"Zixu Liu, Xiao-Jun Zeng, Zhihua Yang","doi":"10.1109/CEC.2018.8477941","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477941","url":null,"abstract":"The penetration of renewable resources in the wholesale electricity market and the demand response in the retail market cause the demand and the supply to become more unpredictable. The ISO is hard to efficiently schedule the production and dispatch the demand. Furthermore, strategic bidding in a more competitive environment is an important problem for the generator. Forecasting the hourly market clearing price (MCP) in the day-ahead electricity market is one of essential task for any bidding decision making. But only a single predicted value of MCP cannot offer enough help for the generator to select the optimal bidding strategies. Aiming at challenge these tasks, we design a new wholesale mechanism in which the ISO declares an interval demand to the wholesale market. The interval demand is more robust than a single demand figure and enables the ISO to handle unpredictable demand under the DR programs. We also developed a forecasting model to forecast a MCP function under the interval demand and introduce the notion of confidence interval to the forecasting model. The confidence interval predicts the exact range of hourly MCP. Based on these work, the optimal bidding strategies for the generator under an interval demand is also illustrated.","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":"114530286","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.2018.8477882
Tianpeng Zhang, K. Szeto
The artificial ant problem [1], [2] describes ants searching for food pellets on a grid using limited knowledge of the local environment. We generalize this model by means of a multi-agent system of communicating ants with intelligence evolved from genetic algorithm. The objective is to find the most food pellets with given energy constraint. A smart ant can ignore the broadcast if it has already collected plenty of food locally, but has received few broadcasts from its teammates lately. On the other hand, if an ant cannot find any food locally, yet some of its teammates are sending out a lot of food broadcast elsewhere, then it may be wise to follow the broadcast and escape the current no-food region. We model this decision strategy on the response to broadcast using genetic algorithm and the result shows that the performance of multiple-ant team in fixed-total-energy search is improved. Since total energy consumed by the team of ants is constant, the number of steps per ant used will be smaller for team with more member, we find that there exists optimal number of team members from simulation. The result depends on both the resource allocated to the team and the food distribution. We distribute food uniformly over an annulus of radius r at the rim of a disk with a bigger radius R, where the ants start their search in the center of the disk. This food distribution provides both a control on the average food density, and a density gradient, while avoiding anisotropic food distribution. This provides a first step to model general food distribution for real application.
{"title":"Optimal Resource Allocation of Communicating Multi-Agent System Using Genetic Algorithm","authors":"Tianpeng Zhang, K. Szeto","doi":"10.1109/CEC.2018.8477882","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477882","url":null,"abstract":"The artificial ant problem [1], [2] describes ants searching for food pellets on a grid using limited knowledge of the local environment. We generalize this model by means of a multi-agent system of communicating ants with intelligence evolved from genetic algorithm. The objective is to find the most food pellets with given energy constraint. A smart ant can ignore the broadcast if it has already collected plenty of food locally, but has received few broadcasts from its teammates lately. On the other hand, if an ant cannot find any food locally, yet some of its teammates are sending out a lot of food broadcast elsewhere, then it may be wise to follow the broadcast and escape the current no-food region. We model this decision strategy on the response to broadcast using genetic algorithm and the result shows that the performance of multiple-ant team in fixed-total-energy search is improved. Since total energy consumed by the team of ants is constant, the number of steps per ant used will be smaller for team with more member, we find that there exists optimal number of team members from simulation. The result depends on both the resource allocated to the team and the food distribution. We distribute food uniformly over an annulus of radius r at the rim of a disk with a bigger radius R, where the ants start their search in the center of the disk. This food distribution provides both a control on the average food density, and a density gradient, while avoiding anisotropic food distribution. This provides a first step to model general food distribution for real application.","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":"114628237","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.2018.8477676
Faramarz Khosravi, M. Borst, J. Teich
Real-world problems often require the simultaneous optimization of multiple, often conflicting, criteria called objectives. Additionally, many of these problems carry on top a wide range of uncertainties in their fitness functions and decision variables, rendering the optimization task even more complex. Several robust optimization techniques do exist to address uncertainty in different aspects of such problems. However, they typically fail to investigate the actual uncertainty distributions while comparing candidate solutions. This paper presents a novel histogram-based approach that enables to compare candidate solutions with arbitrarily distributed uncertain objectives. The proposed comparison operator receives the uncertainty distribution of each objective of two candidate solutions to be compared, and accurately calculates the probability that one objective is greater than the other. Thereby, it enables to determine whether one solution dominates the other. We employ this comparison operator in an existing multi-objective optimization algorithm to allow for finding robust solutions to problems with uncertain objectives. We also extend a well-known multi-objective benchmark suite with various uncertainties, and integrate it together with the proposed comparison operator into an existing framework that incorporates several multi-objective optimization problems and algorithms. Our experiments show that the proposed comparison operator enables achieving better optimization quality and higher robustness compared to the state-of-the-art.
{"title":"Probabilistic Dominance in Robust Multi-Objective Optimization","authors":"Faramarz Khosravi, M. Borst, J. Teich","doi":"10.1109/CEC.2018.8477676","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477676","url":null,"abstract":"Real-world problems often require the simultaneous optimization of multiple, often conflicting, criteria called objectives. Additionally, many of these problems carry on top a wide range of uncertainties in their fitness functions and decision variables, rendering the optimization task even more complex. Several robust optimization techniques do exist to address uncertainty in different aspects of such problems. However, they typically fail to investigate the actual uncertainty distributions while comparing candidate solutions. This paper presents a novel histogram-based approach that enables to compare candidate solutions with arbitrarily distributed uncertain objectives. The proposed comparison operator receives the uncertainty distribution of each objective of two candidate solutions to be compared, and accurately calculates the probability that one objective is greater than the other. Thereby, it enables to determine whether one solution dominates the other. We employ this comparison operator in an existing multi-objective optimization algorithm to allow for finding robust solutions to problems with uncertain objectives. We also extend a well-known multi-objective benchmark suite with various uncertainties, and integrate it together with the proposed comparison operator into an existing framework that incorporates several multi-objective optimization problems and algorithms. Our experiments show that the proposed comparison operator enables achieving better optimization quality and higher robustness compared to the state-of-the-art.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"42 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":"114684827","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.2018.8477758
Yong-Feng Ge, Wei-jie Yu, Zhi-hui Zhan, Jun Zhang
Differential evolution (DE) is a simple and efficient evolutionary algorithm for global optimization. In distributed differential evolution (DDE), the population is divided into several sub-populations and each sub-population evolves independently for enhancing algorithmic performance. Through sharing elite individuals between sub-populations, effective information is spread. However, the information exchanged through individuals is still too limited. To address this issue, a competition-based strategy is proposed in this paper to achieve comprehensive interaction between sub-populations. Two operators named opposition-invasion and cross-invasion are designed to realize the invasion from good performing sub-populations to bad performing subpopulations. By utilizing opposite invading sub-population, the search efficiency at promising regions is improved by opposition-invasion. In cross-invasion, information from both invading and invaded sub-populations is combined and population diversity is maintained. Moreover, the proposed algorithm is implemented in a parallel master-slave manner. Extensive experiments are conducted on 15 widely used large-scale benchmark functions. Experimental results demonstrate that the proposed competition-based DDE (DDE-CB) could achieve competitive or even better performance compared with several state-of-the-art DDE algorithms. The effect of proposed competition-based strategy cooperation with well-known DDE variants is also verified.
{"title":"Competition-Based Distributed Differential Evolution","authors":"Yong-Feng Ge, Wei-jie Yu, Zhi-hui Zhan, Jun Zhang","doi":"10.1109/CEC.2018.8477758","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477758","url":null,"abstract":"Differential evolution (DE) is a simple and efficient evolutionary algorithm for global optimization. In distributed differential evolution (DDE), the population is divided into several sub-populations and each sub-population evolves independently for enhancing algorithmic performance. Through sharing elite individuals between sub-populations, effective information is spread. However, the information exchanged through individuals is still too limited. To address this issue, a competition-based strategy is proposed in this paper to achieve comprehensive interaction between sub-populations. Two operators named opposition-invasion and cross-invasion are designed to realize the invasion from good performing sub-populations to bad performing subpopulations. By utilizing opposite invading sub-population, the search efficiency at promising regions is improved by opposition-invasion. In cross-invasion, information from both invading and invaded sub-populations is combined and population diversity is maintained. Moreover, the proposed algorithm is implemented in a parallel master-slave manner. Extensive experiments are conducted on 15 widely used large-scale benchmark functions. Experimental results demonstrate that the proposed competition-based DDE (DDE-CB) could achieve competitive or even better performance compared with several state-of-the-art DDE algorithms. The effect of proposed competition-based strategy cooperation with well-known DDE variants is also verified.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"16 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":"116962424","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.2018.8477706
G. R. Duarte, Afonso C. C. Lemonge, L. G. Fonseca
The Island Model (IM) is an alternative to implement evolutionary algorithms to be executed in parallel architectures. An important feature of the IM is the process called migration where islands exchange solutions between themselves periodically along iterations of their algorithms. Parameters to be set by the user define how the migration will occur. Different strategies for the migration process have already been proposed and evaluated in the literature. This paper extends the dynamic Island Model (D-IM) proposed in the literature and proposes a new strategy to evaluate the attractiveness of the islands in the model. Some properties of the two configurations for the D-IM were compared. Besides the quality of the solutions, the adjustment of the topology and the movement of solutions between islands were objects of interest in this work.
{"title":"A New Strategy to Evaluate the Attractiveness in a Dynamic Island Model","authors":"G. R. Duarte, Afonso C. C. Lemonge, L. G. Fonseca","doi":"10.1109/CEC.2018.8477706","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477706","url":null,"abstract":"The Island Model (IM) is an alternative to implement evolutionary algorithms to be executed in parallel architectures. An important feature of the IM is the process called migration where islands exchange solutions between themselves periodically along iterations of their algorithms. Parameters to be set by the user define how the migration will occur. Different strategies for the migration process have already been proposed and evaluated in the literature. This paper extends the dynamic Island Model (D-IM) proposed in the literature and proposes a new strategy to evaluate the attractiveness of the islands in the model. Some properties of the two configurations for the D-IM were compared. Besides the quality of the solutions, the adjustment of the topology and the movement of solutions between islands were objects of interest in this work.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"63 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":"116984709","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.2018.8477953
Stefan Forstenlechner, David Fagan, Miguel Nicolau, M. O’Neill
Program synthesis is a complex problem domain tackled by many communities via different methods. In the last few years, a lot of progress has been made with Genetic Programming (GP) on solving a variety of general program synthesis problems for which a benchmark suite has been introduced. While Genetic Programming is capable of finding correct solutions for many problems contained in a general program synthesis problems benchmark suite, the actual success rate per problem is low in most cases. In this paper, we analyse certain aspects of the benchmark suite and the computational effort required to solve its problems. A subset of problems on which GP performs poorly is identified. This subset is analysed to find measures to increase success rates for similar problems. The paper concludes with suggestions to refine performance on program synthesis problems.
{"title":"Towards Understanding and Refining the General Program Synthesis Benchmark Suite with Genetic Programming","authors":"Stefan Forstenlechner, David Fagan, Miguel Nicolau, M. O’Neill","doi":"10.1109/CEC.2018.8477953","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477953","url":null,"abstract":"Program synthesis is a complex problem domain tackled by many communities via different methods. In the last few years, a lot of progress has been made with Genetic Programming (GP) on solving a variety of general program synthesis problems for which a benchmark suite has been introduced. While Genetic Programming is capable of finding correct solutions for many problems contained in a general program synthesis problems benchmark suite, the actual success rate per problem is low in most cases. In this paper, we analyse certain aspects of the benchmark suite and the computational effort required to solve its problems. A subset of problems on which GP performs poorly is identified. This subset is analysed to find measures to increase success rates for similar problems. The paper concludes with suggestions to refine performance on program synthesis problems.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"11 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":"117170166","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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