In this paper, we propose a novel quantum-inspired evolutionary algorithm, called NQEA, for solving combinatorial optimization problems. NQEA uses a new Q-bit update operator to increase the balance between the exploration and exploitation of the search space. In the operator, first, the Q-bits of each individual in the population are updated based on the personal best measurement of that individual and the best measurement of current generation. Then, a restriction is applied to each Q-bit to prevent the premature convergence of its values. The results of experiments on the 0-1 knapsack and NK-landscapes problems show that NQEA performs better than a classical genetic algorithm, CGA, and two quantum-inspired evolutionary algorithms, QEA and vQEA, in terms of convergence speed and accuracy.
{"title":"A novel quantum-inspired evolutionary algorithm for solving combinatorial optimization problems","authors":"Parvaz Mahdabi, M. Abadi, S. Jalili","doi":"10.1145/1569901.1570172","DOIUrl":"https://doi.org/10.1145/1569901.1570172","url":null,"abstract":"In this paper, we propose a novel quantum-inspired evolutionary algorithm, called NQEA, for solving combinatorial optimization problems. NQEA uses a new Q-bit update operator to increase the balance between the exploration and exploitation of the search space. In the operator, first, the Q-bits of each individual in the population are updated based on the personal best measurement of that individual and the best measurement of current generation. Then, a restriction is applied to each Q-bit to prevent the premature convergence of its values. The results of experiments on the 0-1 knapsack and NK-landscapes problems show that NQEA performs better than a classical genetic algorithm, CGA, and two quantum-inspired evolutionary algorithms, QEA and vQEA, in terms of convergence speed and accuracy.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116728189","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}
Biological studies have shown that some species of ants rest quite large fractions of their time. Interestingly, not only single ants show this behaviour, but whole ant colonies exhibit synchronized activity phases resulting from self-organization. Inspired by this behaviour, we previously introduced an adaptive and self-synchronized duty-cycling mechanism for mobile sensor networks with energy harvesting capabilities. In this paper, we focus on the study of this mechanism in the context of static sensor networks, because most sensor networks deployed in practice are static. We consider various scenarios that result from the combination of different network topologies and sizes. Our results show that our mechanism also works in the case of static sensor networks with energy harvesting capabilities.
{"title":"Self-synchronized duty-cycling in sensor networks with energy harvesting capabilities: the static network case","authors":"H. Hernández, C. Blum","doi":"10.1145/1569901.1569907","DOIUrl":"https://doi.org/10.1145/1569901.1569907","url":null,"abstract":"Biological studies have shown that some species of ants rest quite large fractions of their time. Interestingly, not only single ants show this behaviour, but whole ant colonies exhibit synchronized activity phases resulting from self-organization. Inspired by this behaviour, we previously introduced an adaptive and self-synchronized duty-cycling mechanism for mobile sensor networks with energy harvesting capabilities. In this paper, we focus on the study of this mechanism in the context of static sensor networks, because most sensor networks deployed in practice are static. We consider various scenarios that result from the combination of different network topologies and sizes. Our results show that our mechanism also works in the case of static sensor networks with energy harvesting capabilities.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128594308","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}
J. L. Laredo, C. Fernandes, J. J. M. Guervós, Christian Gagné
Despite the intuition that the same population size is not needed throughout the run of an Evolutionary Algorithm (EA), most EAs use a fixed population size. This paper presents an empirical study on the possible benefits of a Simple Variable Population Sizing (SVPS) scheme on the performance of Genetic Algorithms (GAs). It consists in decreasing the population for a GA run following a predetermined schedule, configured by a speed and a severity parameter. The method uses as initial population size an estimation of the minimum size needed to supply enough building blocks, using a fixed-size selectorecombinative GA converging within some confidence interval toward good solutions for a particular problem. Following this methodology, a scalability analysis is conducted on deceptive, quasi-deceptive, and non-deceptive trap functions in order to assess whether SVPS-GA improves performances compared to a fixed-size GA under different problem instances and difficulty levels. Results show several combinations of speed-severity where SVPS-GA preserves the solution quality while improving performances, by reducing the number of evaluations needed for success.
{"title":"Improving genetic algorithms performance via deterministic population shrinkage","authors":"J. L. Laredo, C. Fernandes, J. J. M. Guervós, Christian Gagné","doi":"10.1145/1569901.1570014","DOIUrl":"https://doi.org/10.1145/1569901.1570014","url":null,"abstract":"Despite the intuition that the same population size is not needed throughout the run of an Evolutionary Algorithm (EA), most EAs use a fixed population size. This paper presents an empirical study on the possible benefits of a Simple Variable Population Sizing (SVPS) scheme on the performance of Genetic Algorithms (GAs). It consists in decreasing the population for a GA run following a predetermined schedule, configured by a speed and a severity parameter. The method uses as initial population size an estimation of the minimum size needed to supply enough building blocks, using a fixed-size selectorecombinative GA converging within some confidence interval toward good solutions for a particular problem. Following this methodology, a scalability analysis is conducted on deceptive, quasi-deceptive, and non-deceptive trap functions in order to assess whether SVPS-GA improves performances compared to a fixed-size GA under different problem instances and difficulty levels. Results show several combinations of speed-severity where SVPS-GA preserves the solution quality while improving performances, by reducing the number of evaluations needed for success.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"315 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128628467","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}
Multi-material 3D-printing technologies permit the freeform fabrication of complex spatial arrangements of materials in arbitrary geometries. This technology has opened the door to a large mechanical design space with many novel yet non-intuitive possibilities. This space is not easily searched using conventional topological optimization methods such as homogenization. Here we present an evolutionary design process for three-dimensional multi-material structures that explores this design space and designs substructures tailored for custom functionalities. The algorithm is demonstrated for the design of 3D non-uniform beams and 3D compliant actuators.
{"title":"Multi material topological optimization of structures and mechanisms","authors":"J. Hiller, Hod Lipson","doi":"10.1145/1569901.1570105","DOIUrl":"https://doi.org/10.1145/1569901.1570105","url":null,"abstract":"Multi-material 3D-printing technologies permit the freeform fabrication of complex spatial arrangements of materials in arbitrary geometries. This technology has opened the door to a large mechanical design space with many novel yet non-intuitive possibilities. This space is not easily searched using conventional topological optimization methods such as homogenization. Here we present an evolutionary design process for three-dimensional multi-material structures that explores this design space and designs substructures tailored for custom functionalities. The algorithm is demonstrated for the design of 3D non-uniform beams and 3D compliant actuators.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129543303","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}
We study the update of the distribution in Estimation of Distribution Algorithms, and show that a simple modification leads to unbiased estimates of the optimum. The simple modification (based on a proper reweighting of estimates) leads to a strongly improved behavior in front of premature convergence.
{"title":"Why one must use reweighting in estimation of distribution algorithms","authors":"F. Teytaud, O. Teytaud","doi":"10.1145/1569901.1569964","DOIUrl":"https://doi.org/10.1145/1569901.1569964","url":null,"abstract":"We study the update of the distribution in Estimation of Distribution Algorithms, and show that a simple modification leads to unbiased estimates of the optimum. The simple modification (based on a proper reweighting of estimates) leads to a strongly improved behavior in front of premature convergence.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116330257","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}
This paper presents a framework of estimation of particle swarm distribution algorithms (EPSDAs). The aim lies in effectively combining particle swarm optimization (PSO) with estimation of distribution algorithms (EDAs) without losing on their unique features. To exhibit its practicability, an extended compact particle swarm optimization (EcPSO) is developed along the lines of the suggested framework. Empirical results have adduced grounds for its effectiveness.
{"title":"Estimation of particle swarm distribution algorithms: bringing together the strengths of PSO and EDAs","authors":"C. Ahn, Hyun-Tae Kim","doi":"10.1145/1569901.1570178","DOIUrl":"https://doi.org/10.1145/1569901.1570178","url":null,"abstract":"This paper presents a framework of estimation of particle swarm distribution algorithms (EPSDAs). The aim lies in effectively combining particle swarm optimization (PSO) with estimation of distribution algorithms (EDAs) without losing on their unique features. To exhibit its practicability, an extended compact particle swarm optimization (EcPSO) is developed along the lines of the suggested framework. Empirical results have adduced grounds for its effectiveness.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121501724","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}
The aircraft arrival sequencing and scheduling (ASS) problem is one of the most significant problems in the air traffic control (ATC). This paper makes the first attempt to design an ant colony system (ACS) based approach to solve this NP-hard problem. In order to reduce the computational effort of the optimization process, the receding horizon control (RHC) strategy is integrated into the ACS to divide the optimization process into several sub-processes and solve them one by one. This strategy can reduce the problem scale in each sub-optimization process, resulting in lighter computational effort and higher quality solution for the whole problem. Experiments are conducted to demonstrate the effectiveness and efficiency of the proposed RHC based ACS algorithm for the ASS problem (RHC-ACS-ASS). Simulation results show that the RHC-ACS-ASS not only outperforms the GA based approaches, but also the ACS based approach without using the RHC strategy.
{"title":"Ant colony system based on receding horizon control for aircraft arrival sequencing and scheduling","authors":"Zhi-hui Zhan, Jun Zhang, Yue-jiao Gong","doi":"10.1145/1569901.1570148","DOIUrl":"https://doi.org/10.1145/1569901.1570148","url":null,"abstract":"The aircraft arrival sequencing and scheduling (ASS) problem is one of the most significant problems in the air traffic control (ATC). This paper makes the first attempt to design an ant colony system (ACS) based approach to solve this NP-hard problem. In order to reduce the computational effort of the optimization process, the receding horizon control (RHC) strategy is integrated into the ACS to divide the optimization process into several sub-processes and solve them one by one. This strategy can reduce the problem scale in each sub-optimization process, resulting in lighter computational effort and higher quality solution for the whole problem. Experiments are conducted to demonstrate the effectiveness and efficiency of the proposed RHC based ACS algorithm for the ASS problem (RHC-ACS-ASS). Simulation results show that the RHC-ACS-ASS not only outperforms the GA based approaches, but also the ACS based approach without using the RHC strategy.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114698698","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}
Efficient Natural Evolution Strategies (eNES) is a novel alternative to conventional evolutionary algorithms, using the natural gradient to adapt the mutation distribution. Unlike previous methods based on natural gradients, eNES uses a fast algorithm to calculate the inverse of the exact Fisher information matrix, thus increasing both robustness and performance of its evolution gradient estimation, even in higher dimensions. Additional novel aspects of eNES include optimal fitness baselines and importance mixing (a procedure for updating the population with very few fitness evaluations). The algorithm yields competitive results on both unimodal and multimodal benchmarks.
{"title":"Efficient natural evolution strategies","authors":"Yi Sun, Daan Wierstra, T. Schaul, J. Schmidhuber","doi":"10.1145/1569901.1569976","DOIUrl":"https://doi.org/10.1145/1569901.1569976","url":null,"abstract":"Efficient Natural Evolution Strategies (eNES) is a novel alternative to conventional evolutionary algorithms, using the natural gradient to adapt the mutation distribution. Unlike previous methods based on natural gradients, eNES uses a fast algorithm to calculate the inverse of the exact Fisher information matrix, thus increasing both robustness and performance of its evolution gradient estimation, even in higher dimensions. Additional novel aspects of eNES include optimal fitness baselines and importance mixing (a procedure for updating the population with very few fitness evaluations). The algorithm yields competitive results on both unimodal and multimodal benchmarks.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124366600","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}
We propose a representation of the stock-trading market as a group of rule-based trading agents, with the agents evolved using past prices. We encode each rule-based agent as a genome, and then describe how a steady-state genetic algorithm can evolve a group of these genomes (i.e. an inverted market) using past stock prices. This market is then used to generate forecasts of future stocks prices, which are compared to actual future stock prices. We show how our method outperforms standard financial time-series forecasting models, such as ARIMA and Lognormal, on actual stock price data taken from real-world archives. Track: Real World Applications (RWA).
{"title":"Evolutionary inference of rule-based trading agents from real-world stock price histories and their use in forecasting","authors":"L. Charbonneau, N. Kharma","doi":"10.1145/1569901.1570097","DOIUrl":"https://doi.org/10.1145/1569901.1570097","url":null,"abstract":"We propose a representation of the stock-trading market as a group of rule-based trading agents, with the agents evolved using past prices. We encode each rule-based agent as a genome, and then describe how a steady-state genetic algorithm can evolve a group of these genomes (i.e. an inverted market) using past stock prices. This market is then used to generate forecasts of future stocks prices, which are compared to actual future stock prices. We show how our method outperforms standard financial time-series forecasting models, such as ARIMA and Lognormal, on actual stock price data taken from real-world archives. Track: Real World Applications (RWA).","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131385136","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}
A new approach to extract useful design information from non-dominated solutions of real-world multiobjective optimization problems is proposed. The proposed approach enables an analysis of line, face, or volume data that Pareto-optimal solutions have such as flow field and stress distribution by decomposing the data into principal modes using proper orthogonal decomposition. Analysis of the shape and surface pressure data of the non-dominated solutions of an aerodynamic transonic airfoil shape optimization problem shows capability of the proposed approach for design knowledge extraction for real-world design optimization problems.
{"title":"Data mining of non-dominated solutions using proper orthogonal decomposition","authors":"A. Oyama, T. Nonomura, K. Fujii","doi":"10.1145/1569901.1570245","DOIUrl":"https://doi.org/10.1145/1569901.1570245","url":null,"abstract":"A new approach to extract useful design information from non-dominated solutions of real-world multiobjective optimization problems is proposed. The proposed approach enables an analysis of line, face, or volume data that Pareto-optimal solutions have such as flow field and stress distribution by decomposing the data into principal modes using proper orthogonal decomposition. Analysis of the shape and surface pressure data of the non-dominated solutions of an aerodynamic transonic airfoil shape optimization problem shows capability of the proposed approach for design knowledge extraction for real-world design optimization problems.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130048112","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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