The process of creating city designs is a complex, time-consuming endeavor. This paper presents CityBreeder, a system which uses Evolutionary Computation to enable the rapid, user-guided development of city designs based on the blending of multiple existing designs.
{"title":"CityBreeder: city design with evolutionary computation","authors":"Adam S Cohen, T. White","doi":"10.1145/2598394.2598495","DOIUrl":"https://doi.org/10.1145/2598394.2598495","url":null,"abstract":"The process of creating city designs is a complex, time-consuming endeavor. This paper presents CityBreeder, a system which uses Evolutionary Computation to enable the rapid, user-guided development of city designs based on the blending of multiple existing designs.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133686375","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}
E. H. V. Segundo, Gabriel Fiori Neto, A. M. D. Silva, V. Mariani, L. Coelho
The gravitational search algorithm (GSA) is a stochastic population-based metaheuristic inspired by the interaction of masses via Newtonian gravity law. In this paper, we propose a modified GSA (MGSA) based on logarithm and Gaussian signals for enhancing the performance of standard GSA. To evaluate the performance of the proposed MGSA, well-known benchmark functions in the literature are optimized using the proposed MGSA, and provides comparisons with the standard GSA.
{"title":"A modified gravitational search algorithm for continuous optimization","authors":"E. H. V. Segundo, Gabriel Fiori Neto, A. M. D. Silva, V. Mariani, L. Coelho","doi":"10.1145/2598394.2602281","DOIUrl":"https://doi.org/10.1145/2598394.2602281","url":null,"abstract":"The gravitational search algorithm (GSA) is a stochastic population-based metaheuristic inspired by the interaction of masses via Newtonian gravity law. In this paper, we propose a modified GSA (MGSA) based on logarithm and Gaussian signals for enhancing the performance of standard GSA. To evaluate the performance of the proposed MGSA, well-known benchmark functions in the literature are optimized using the proposed MGSA, and provides comparisons with the standard GSA.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"190 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134122866","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}
Memetic search is well known as one of the state-of-the-art metaheuristics for finding high-quality solutions to NP-hard problems. Its performance is often attributable to appropriate design, including the choice of its operators. In this paper, we propose a Markov Decision Process model for the selection of crossover operators in the course of the evolutionary search. We solve the proposed model by a Q-learning method. We experimentally verify the efficacy of our proposed approach on the benchmark instances of Quadratic Assignment Problem.
{"title":"Reinforcement learning for adaptive operator selection in memetic search applied to quadratic assignment problem","authors":"S. D. Handoko, D. Nguyen, Z. Yuan, H. Lau","doi":"10.1145/2598394.2598451","DOIUrl":"https://doi.org/10.1145/2598394.2598451","url":null,"abstract":"Memetic search is well known as one of the state-of-the-art metaheuristics for finding high-quality solutions to NP-hard problems. Its performance is often attributable to appropriate design, including the choice of its operators. In this paper, we propose a Markov Decision Process model for the selection of crossover operators in the course of the evolutionary search. We solve the proposed model by a Q-learning method. We experimentally verify the efficacy of our proposed approach on the benchmark instances of Quadratic Assignment Problem.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133882283","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}
Mark Wineberg is an Associate Professor at the Univeristy of Guelph. He has been actively researching the field of GEC since 1993 while he was still a graduate student. Over the years he has published on various topics including: the intersection of GA and GP, enhancing the GA for improved behavior in dynamic environments through specialized multiple populations, and exploring the concept of distances and diversity in GA populations. Prof. Wineberg also teaches an undergraduate course on computer simulation and modeling of discrete stochastic systems with an emphasis on proper statistical analysis, as well as a graduate course on experimental design and analysis for computer science, which is an outgrowth of the statistical analysis tutorial given at GECCO.
Mark Wineberg是圭尔夫大学的副教授。他从1993年开始积极研究GEC领域，当时他还是一名研究生。多年来，他发表了各种主题的文章，包括:遗传算法和遗传算法的交叉，通过专门的多种群增强遗传算法以改善动态环境中的行为，以及探索遗传算法种群的距离和多样性概念。Wineberg教授还教授一门关于离散随机系统的计算机模拟和建模的本科课程，重点是适当的统计分析，以及一门关于计算机科学实验设计和分析的研究生课程，这是GECCO提供的统计分析教程的成果。
{"title":"Statistical analysis for evolutionary computation: an introduction","authors":"M. Wineberg","doi":"10.1145/2598394.2605341","DOIUrl":"https://doi.org/10.1145/2598394.2605341","url":null,"abstract":"Mark Wineberg is an Associate Professor at the Univeristy of Guelph. He has been actively researching the field of GEC since 1993 while he was still a graduate student. Over the years he has published on various topics including: the intersection of GA and GP, enhancing the GA for improved behavior in dynamic environments through specialized multiple populations, and exploring the concept of distances and diversity in GA populations. Prof. Wineberg also teaches an undergraduate course on computer simulation and modeling of discrete stochastic systems with an emphasis on proper statistical analysis, as well as a graduate course on experimental design and analysis for computer science, which is an outgrowth of the statistical analysis tutorial given at GECCO.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115689538","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}
When data is released or shared among different organizations, some sensitive or confidential information may be subject to be exposed by using data mining tools. Thus, a question arises: how can we protect sensitive knowledge while allowing other parties to extract the knowledge behind the shared data. In this paper, we address the problem of privacy preserving in association rule mining from the perspective of multi-objective optimization. A sensitive rule can be hidden by adding items into the dataset to make the support of the antecedent part of the sensitive rule increase and accordingly the confidence of the sensitive rule decrease. The evolutionary multi-objective optimization (EMO) algorithm is utilized to find suitable transactions (or tuples) to be modified so as the side effects to be minimized. Experiments on real datasets demonstrated the effectiveness of the proposed method.
{"title":"Use EMO to protect sensitive knowledge in association rule mining by adding items","authors":"Peng Cheng, Jeng-Shyang Pan","doi":"10.1145/2598394.2598465","DOIUrl":"https://doi.org/10.1145/2598394.2598465","url":null,"abstract":"When data is released or shared among different organizations, some sensitive or confidential information may be subject to be exposed by using data mining tools. Thus, a question arises: how can we protect sensitive knowledge while allowing other parties to extract the knowledge behind the shared data. In this paper, we address the problem of privacy preserving in association rule mining from the perspective of multi-objective optimization. A sensitive rule can be hidden by adding items into the dataset to make the support of the antecedent part of the sensitive rule increase and accordingly the confidence of the sensitive rule decrease. The evolutionary multi-objective optimization (EMO) algorithm is utilized to find suitable transactions (or tuples) to be modified so as the side effects to be minimized. Experiments on real datasets demonstrated the effectiveness of the proposed method.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114525295","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}
MEMS layout optimization is a typical multi-objective constrained optimization problem. This paper proposes an improved MOEA called cMOEA/D to solve this problem. The cMOEA/D is based on MOEA/D but also uses the frequency of individual update of sub-problems to locate the promising sub-problems. By dynamically allocating computing resources to more promising sub-problems, we can effectively improve the performance of the algorithm to find more non-dominated solutions in MEMS layout optimization. In addition, we compared two mechanisms of constraint handling, Stochastic Ranking (SR) and Constraint-domination principle (CDP). The experimental results show that CDP works better than SR and the proposed algorithm outperforms the state-of-art algorithms such as NSGA-II and MOEA/D, in terms of convergence and diversity.
{"title":"Design optimization of MEMS using constrained multi-objective evolutionary algorithm","authors":"Wenji Li, Zhun Fan, Xinye Cai, Huibiao Lin, Shuxiang Xie, Sheng Wang","doi":"10.1145/2598394.2610010","DOIUrl":"https://doi.org/10.1145/2598394.2610010","url":null,"abstract":"MEMS layout optimization is a typical multi-objective constrained optimization problem. This paper proposes an improved MOEA called cMOEA/D to solve this problem. The cMOEA/D is based on MOEA/D but also uses the frequency of individual update of sub-problems to locate the promising sub-problems. By dynamically allocating computing resources to more promising sub-problems, we can effectively improve the performance of the algorithm to find more non-dominated solutions in MEMS layout optimization. In addition, we compared two mechanisms of constraint handling, Stochastic Ranking (SR) and Constraint-domination principle (CDP). The experimental results show that CDP works better than SR and the proposed algorithm outperforms the state-of-art algorithms such as NSGA-II and MOEA/D, in terms of convergence and diversity.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116108804","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}
In this paper, we propose a multiobjective probabilistic Pareto local search to address combinatorial optimization problems (COPs). The probability is determined by the success counts of local search offspring entering an external domination archive and this probabilistic information is used to further guide the selection of promising solutions for Pareto local search. In addition, simulated annealing is integrated in this framework as the local refinement process. This multiobjective probabilistic Pareto local search algorithm (MOPPLS), is tested on two famous COPs and compared with some well-known multiobjective evolutionary algorithms. Experimental results suggest that MOPPLS outperforms other compared algorithms.
{"title":"A probabilistic pareto local search based on historical success counting for multiobjective optimization","authors":"Xinye Cai, Xin Cheng, Zhun Fan","doi":"10.1145/2598394.2610011","DOIUrl":"https://doi.org/10.1145/2598394.2610011","url":null,"abstract":"In this paper, we propose a multiobjective probabilistic Pareto local search to address combinatorial optimization problems (COPs). The probability is determined by the success counts of local search offspring entering an external domination archive and this probabilistic information is used to further guide the selection of promising solutions for Pareto local search. In addition, simulated annealing is integrated in this framework as the local refinement process. This multiobjective probabilistic Pareto local search algorithm (MOPPLS), is tested on two famous COPs and compared with some well-known multiobjective evolutionary algorithms. Experimental results suggest that MOPPLS outperforms other compared algorithms.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"247 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115250759","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}
To construct graphs whose quality results from complicated relationships that pervade the entire graph, especially relationships at multiple scales, follow a strategy of repeatedly making local patches to a single graph. Look for small, easily recognized flaws in local areas of the graph and fix them. Add tags to the graph to represent non-local relationships and higher-level structures as individual nodes. The tags then have easily recognized flaws that relate to non-local and higher-level concerns, enabling local patching to set off cascades of local fixes that address those concerns.
{"title":"Structural stigmergy: a speculative pattern language for metaheuristics","authors":"Ben Kovitz, J. Swan","doi":"10.1145/2598394.2609845","DOIUrl":"https://doi.org/10.1145/2598394.2609845","url":null,"abstract":"To construct graphs whose quality results from complicated relationships that pervade the entire graph, especially relationships at multiple scales, follow a strategy of repeatedly making local patches to a single graph. Look for small, easily recognized flaws in local areas of the graph and fix them. Add tags to the graph to represent non-local relationships and higher-level structures as individual nodes. The tags then have easily recognized flaws that relate to non-local and higher-level concerns, enabling local patching to set off cascades of local fixes that address those concerns.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115444721","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}
In this talk I will show how artificial evolution can be used to address biological questions and explain phenomena for which there is no fossil record or no experimental evidence, such evolution of behavior, altruism, and communication. I will give examples related to insects and plants. Central to this endeavor is how selection mechanisms are applied and interpreted. I will also show how selection pressure can be lifted in artificial evolution and lead to open-ended evolution in dynamic and changing environments.
{"title":"Bridging natural and artificial evolution","authors":"D. Floreano","doi":"10.1145/2598394.2598396","DOIUrl":"https://doi.org/10.1145/2598394.2598396","url":null,"abstract":"In this talk I will show how artificial evolution can be used to address biological questions and explain phenomena for which there is no fossil record or no experimental evidence, such evolution of behavior, altruism, and communication. I will give examples related to insects and plants. Central to this endeavor is how selection mechanisms are applied and interpreted. I will also show how selection pressure can be lifted in artificial evolution and lead to open-ended evolution in dynamic and changing environments.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123117685","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}
Geometric Semantic Genetic Programming (GSGP) is a recently defined form of Genetic Programming (GP) that has shown promising results on single output Boolean problems when compared with standard tree-based GP. In this paper we compare GSGP with Cartesian GP (CGP) on comprehensive set of Boolean benchmarks, consisting of both single and multiple outputs Boolean problems. The results obtained show that GSGP outperforms also CGP, confirming the efficacy of GSGP in solving Boolean problems.
{"title":"A comparison between geometric semantic GP and cartesian GP for boolean functions learning","authors":"A. Mambrini, L. Manzoni","doi":"10.1145/2598394.2598475","DOIUrl":"https://doi.org/10.1145/2598394.2598475","url":null,"abstract":"Geometric Semantic Genetic Programming (GSGP) is a recently defined form of Genetic Programming (GP) that has shown promising results on single output Boolean problems when compared with standard tree-based GP. In this paper we compare GSGP with Cartesian GP (CGP) on comprehensive set of Boolean benchmarks, consisting of both single and multiple outputs Boolean problems. The results obtained show that GSGP outperforms also CGP, confirming the efficacy of GSGP in solving Boolean problems.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"49 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123179161","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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