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}
Reliability is one of the important measures of how well a system meets its design objective, and mathematically is the probability that a system will perform satisfactorily for a given period of time. When the system is described by a network of N components (nodes) and L connections (links), the reliability of the system becomes a network design problem that is an NP-hard combinatorial optimization problem. In this paper, genetic algorithm is applied to find the most reliable connected network with the same connectivity, (i.e. with given N and L). The accuracy and efficiency of genetic algorithm in the search of the most reliable network(s) of same connectivity is verified by exhaustive search. Our results not only demonstrate the efficiency of our algorithm for optimization problem for graphs, but also suggest that the most reliable network will have high symmetry.
{"title":"Search for the most reliable network of fixed connectivity using genetic algorithm","authors":"Ho Tat Lam, K. Szeto","doi":"10.1145/2598394.2598412","DOIUrl":"https://doi.org/10.1145/2598394.2598412","url":null,"abstract":"Reliability is one of the important measures of how well a system meets its design objective, and mathematically is the probability that a system will perform satisfactorily for a given period of time. When the system is described by a network of N components (nodes) and L connections (links), the reliability of the system becomes a network design problem that is an NP-hard combinatorial optimization problem. In this paper, genetic algorithm is applied to find the most reliable connected network with the same connectivity, (i.e. with given N and L). The accuracy and efficiency of genetic algorithm in the search of the most reliable network(s) of same connectivity is verified by exhaustive search. Our results not only demonstrate the efficiency of our algorithm for optimization problem for graphs, but also suggest that the most reliable network will have high symmetry.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"280 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":"123067493","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}
M. Kommenda, M. Affenzeller, Bogdan Burlacu, G. Kronberger, Stephan M. Winkler
In this publication genetic programming (GP) with data migration for symbolic regression is presented. The motivation for the development of the algorithm is to evolve models which generalize well on previously unseen data. GP with data migration uses multiple subpopulations to maintain the genetic diversity during the algorithm run and a sophisticated training subset selection strategy. Each subpopulation is evaluated on a different fixed training subset (FTS) and additionally a variable training subset (VTS) is exchanged between the subpopulations at specific data migration intervals. Thus, the individuals are evaluated on the unification of FTS and VTS and should have better generalization properties due to the regular changes of the VTS. The implemented algorithm is compared to several GP variants on a number of symbolic regression benchmark problems to test the effectiveness of the multiple populations and data migration strategy. Additionally, different algorithm configurations and migration strategies are evaluated to show their impact with respect to the achieved quality.
{"title":"Genetic programming with data migration for symbolic regression","authors":"M. Kommenda, M. Affenzeller, Bogdan Burlacu, G. Kronberger, Stephan M. Winkler","doi":"10.1145/2598394.2609857","DOIUrl":"https://doi.org/10.1145/2598394.2609857","url":null,"abstract":"In this publication genetic programming (GP) with data migration for symbolic regression is presented. The motivation for the development of the algorithm is to evolve models which generalize well on previously unseen data. GP with data migration uses multiple subpopulations to maintain the genetic diversity during the algorithm run and a sophisticated training subset selection strategy. Each subpopulation is evaluated on a different fixed training subset (FTS) and additionally a variable training subset (VTS) is exchanged between the subpopulations at specific data migration intervals. Thus, the individuals are evaluated on the unification of FTS and VTS and should have better generalization properties due to the regular changes of the VTS. The implemented algorithm is compared to several GP variants on a number of symbolic regression benchmark problems to test the effectiveness of the multiple populations and data migration strategy. Additionally, different algorithm configurations and migration strategies are evaluated to show their impact with respect to the achieved quality.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"17 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":"131486294","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}
{"title":"Theory of swarm intelligence","authors":"Dirk Sudholt","doi":"10.1145/2598394.2605354","DOIUrl":"https://doi.org/10.1145/2598394.2605354","url":null,"abstract":"","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"84 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":"132418327","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}
Learning Classifier Systems were introduced in the 1970s by John H. Holland as highly adaptive, cognitive systems. More than 40 years later, the introduction of Stewart W. Wilson's XCS, a highly engineered classifier system model, has transformed them into a state-of-the-art machine learning system. Learning classifier systems can effectively solve data-mining problems, reinforcement learning problems, and also cognitive, robotics control problems. In comparison to other, non-evolutionary machine learning techniques, their performance is competitive or superior, dependent on the setup and problem. Learning classifier systems can work both online and offline, they are extremely flexible, applicable to a larger range of problems, and are highly adaptive. Moreover, system knowledge can be easily extracted, visualized, or even used to focus the progressive search on particular interesting subspaces. This tutorial provides a gentle introduction to learning classifier systems and their general functionality. It then surveys the current theoretical understanding of the systems. Finally, we provide a suite of current successful LCS applications and discuss the most promising areas for future applications and research directions.
学习分类器系统是由John H. Holland在20世纪70年代引入的，是一种高度自适应的认知系统。40多年后，Stewart W. Wilson的XCS(一种高度工程化的分类器系统模型)的引入，将它们转变为最先进的机器学习系统。学习分类器系统可以有效地解决数据挖掘问题、强化学习问题以及认知、机器人控制问题。与其他非进化机器学习技术相比，它们的性能是有竞争力的还是更好的，这取决于设置和问题。学习分类器系统可以在线和离线工作，它们非常灵活，适用于更大范围的问题，并且具有高度的适应性。此外，系统知识可以很容易地提取、可视化，甚至用于将逐步搜索集中在特定感兴趣的子空间上。本教程提供了学习分类器系统及其一般功能的简单介绍。然后调查了当前对系统的理论认识。最后，我们提供了一套目前成功的LCS应用，并讨论了未来最有希望的应用领域和研究方向。
{"title":"Learning classifier systems: a gentle introduction","authors":"P. Lanzi","doi":"10.1145/2598394.2605343","DOIUrl":"https://doi.org/10.1145/2598394.2605343","url":null,"abstract":"Learning Classifier Systems were introduced in the 1970s by John H. Holland as highly adaptive, cognitive systems. More than 40 years later, the introduction of Stewart W. Wilson's XCS, a highly engineered classifier system model, has transformed them into a state-of-the-art machine learning system. Learning classifier systems can effectively solve data-mining problems, reinforcement learning problems, and also cognitive, robotics control problems. In comparison to other, non-evolutionary machine learning techniques, their performance is competitive or superior, dependent on the setup and problem. Learning classifier systems can work both online and offline, they are extremely flexible, applicable to a larger range of problems, and are highly adaptive. Moreover, system knowledge can be easily extracted, visualized, or even used to focus the progressive search on particular interesting subspaces. This tutorial provides a gentle introduction to learning classifier systems and their general functionality. It then surveys the current theoretical understanding of the systems. Finally, we provide a suite of current successful LCS applications and discuss the most promising areas for future applications and research directions.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"37 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":"133259028","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 application of genetic and evolutionary computation to problems in medicine has increased rapidly over the past five years, but there are specific issues and challenges that distinguish it from other real-world applications. Obtaining reliable and coherent patient data, establishing the clinical need and demonstrating value in the results obtained are all aspects that require careful and detailed consideration. This tutorial is based on research which uses genetic programming (a representation of Cartesian Genetic Programming) in the diagnosis and monitoring of Parkinson's disease, Alzheimer's disease and other neurodegenerative conditions, as well as in the early detection of breast cancer through automated assessment of mammograms. The work is supported by multiple clinical studies in progress in the UK (Leeds General Infirmary), USA (UCSF), UAE (Dubai Rashid Hospital), Australia (Monash Medical Center) and Singapore (National Neuroscience Institute). The technology is protected through three patent applications and a University spin-out company marketing four medical devices. The tutorial considers the following topics: Introduction to medical applications of genetic and evolutionary computation and how these differ from other real-world applications Overview of past work in the from a medical and evolutionary computation point of view Three case examples of medical applications: i. diagnosis and monitoring of Parkinson's disease ii. detection of beast cancer from mammograms iii. cancer screening using Raman spectroscopy Practical advice on how to get started working on medical applications, including existing medical databases and conducting new medical studies, commercialization and protecting intellectual property. Summary, further reading and links
{"title":"Medical applications of evolutionary computation","authors":"S. Smith","doi":"10.1145/2598394.2605364","DOIUrl":"https://doi.org/10.1145/2598394.2605364","url":null,"abstract":"The application of genetic and evolutionary computation to problems in medicine has increased rapidly over the past five years, but there are specific issues and challenges that distinguish it from other real-world applications. Obtaining reliable and coherent patient data, establishing the clinical need and demonstrating value in the results obtained are all aspects that require careful and detailed consideration. This tutorial is based on research which uses genetic programming (a representation of Cartesian Genetic Programming) in the diagnosis and monitoring of Parkinson's disease, Alzheimer's disease and other neurodegenerative conditions, as well as in the early detection of breast cancer through automated assessment of mammograms. The work is supported by multiple clinical studies in progress in the UK (Leeds General Infirmary), USA (UCSF), UAE (Dubai Rashid Hospital), Australia (Monash Medical Center) and Singapore (National Neuroscience Institute). The technology is protected through three patent applications and a University spin-out company marketing four medical devices. The tutorial considers the following topics: Introduction to medical applications of genetic and evolutionary computation and how these differ from other real-world applications Overview of past work in the from a medical and evolutionary computation point of view Three case examples of medical applications: i. diagnosis and monitoring of Parkinson's disease ii. detection of beast cancer from mammograms iii. cancer screening using Raman spectroscopy Practical advice on how to get started working on medical applications, including existing medical databases and conducting new medical studies, commercialization and protecting intellectual property. Summary, further reading and links","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"6 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":"133593330","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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