This paper presents a multi-population genetic algorithm for procedural generation of levels for platform games such as Super Mario Bros (SMB). The algorithm evolves four aspects of the game during its generations: terrain, enemies, coins and blocks. Each aspect has its own codification, population and fitness function. At the end of the evolution, the best four aspects are combined to construct the level. The method has as input a vector of parameters to configure the characteristics of each aspect. Experiments were made to evaluate the capability of the method in generating interesting levels. Results showed the method can be controlled to generate different types of levels.
{"title":"A multi-population genetic algorithm for procedural generation of levels for platform games","authors":"Lucas N. Ferreira, L. T. Pereira, C. Toledo","doi":"10.1145/2598394.2598489","DOIUrl":"https://doi.org/10.1145/2598394.2598489","url":null,"abstract":"This paper presents a multi-population genetic algorithm for procedural generation of levels for platform games such as Super Mario Bros (SMB). The algorithm evolves four aspects of the game during its generations: terrain, enemies, coins and blocks. Each aspect has its own codification, population and fitness function. At the end of the evolution, the best four aspects are combined to construct the level. The method has as input a vector of parameters to configure the characteristics of each aspect. Experiments were made to evaluate the capability of the method in generating interesting levels. Results showed the method can be controlled to generate different types of levels.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"20 4 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":"124969277","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}
Find loads of the an introduction to genetic algorithms book catalogues in this site as the choice of you visiting this page. You can also join to the website book library that will show you numerous books from any types. Literature, science, politics, and many more catalogues are presented to offer you the best book to find. The book that really makes you feels satisfied. Or that's the book that will save you from your job deadline.
{"title":"Introduction to genetic algorithms","authors":"E. Goodman","doi":"10.1145/2598394.2605335","DOIUrl":"https://doi.org/10.1145/2598394.2605335","url":null,"abstract":"Find loads of the an introduction to genetic algorithms book catalogues in this site as the choice of you visiting this page. You can also join to the website book library that will show you numerous books from any types. Literature, science, politics, and many more catalogues are presented to offer you the best book to find. The book that really makes you feels satisfied. Or that's the book that will save you from your job deadline.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"26 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":"129462741","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}
Real world machine learning, where data is sampled continuously, may in theory be classifiable into distinct and unchanging categories but in practice the classification becomes non-trivial because the nature of the background noise continuously changes. Applying distinct and unchanging categories for data ignores the fact that for some applications where the categories of data may remain constant, the background noise constantly changes, and thus the ability for a supervised learning method to work is limited. In this work, we propose a novel method based on an Artificial Immune System (AIS) and implemented on a systemic computer, which is designed to adapt itself over continuous arrival of data to cope with changing patterns of noise without requirement for feedback, as a result of its own experience.
{"title":"Adapting to dynamically changing noise during learning of heart sounds: an AIS-based approach using systemic computation","authors":"Yiqi Deng, P. Bentley","doi":"10.1145/2598394.2598461","DOIUrl":"https://doi.org/10.1145/2598394.2598461","url":null,"abstract":"Real world machine learning, where data is sampled continuously, may in theory be classifiable into distinct and unchanging categories but in practice the classification becomes non-trivial because the nature of the background noise continuously changes. Applying distinct and unchanging categories for data ignores the fact that for some applications where the categories of data may remain constant, the background noise constantly changes, and thus the ability for a supervised learning method to work is limited. In this work, we propose a novel method based on an Artificial Immune System (AIS) and implemented on a systemic computer, which is designed to adapt itself over continuous arrival of data to cope with changing patterns of noise without requirement for feedback, as a result of its own experience.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"58 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":"129463344","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}
Bin Jiang, Lei Wang, Chao Yang, Shuming Peng, Renfa Li
Development of Internet technology has made the use of email to be one of the predominant means of communication in the information society. Information exchange among people via email service has produced lots of communication data, which have been widely used in research about information propagation on virtual social networks. The focus of this paper is on the "Enron Email Dataset". The ideas discussed gave thorough consideration to the diversity of organizational positions' attributes, the dynamic behaviors of users to select information contents and communication partners via email service. We then established a quantitative analysis on the multiple interactive relationships of the email communication network. Further, an agent-based model for modeling the information diffusion in an organization via email communication network was proposed, by relating the microscopic individual behaviors and the macroscopic system evolution. Based on the simulation experiments, we analyzed and compared the topological characteristics and evaluative patterns of our model with the Enron Email Dataset. The experimental results proved that our model was beneficial to uncover the implicit communication mechanisms of a real organization.
{"title":"Modeling the information propagation in an email communication network using an agent-based approach","authors":"Bin Jiang, Lei Wang, Chao Yang, Shuming Peng, Renfa Li","doi":"10.1145/2598394.2610013","DOIUrl":"https://doi.org/10.1145/2598394.2610013","url":null,"abstract":"Development of Internet technology has made the use of email to be one of the predominant means of communication in the information society. Information exchange among people via email service has produced lots of communication data, which have been widely used in research about information propagation on virtual social networks. The focus of this paper is on the \"Enron Email Dataset\". The ideas discussed gave thorough consideration to the diversity of organizational positions' attributes, the dynamic behaviors of users to select information contents and communication partners via email service. We then established a quantitative analysis on the multiple interactive relationships of the email communication network. Further, an agent-based model for modeling the information diffusion in an organization via email communication network was proposed, by relating the microscopic individual behaviors and the macroscopic system evolution. Based on the simulation experiments, we analyzed and compared the topological characteristics and evaluative patterns of our model with the Enron Email Dataset. The experimental results proved that our model was beneficial to uncover the implicit communication mechanisms of a real organization.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"83 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":"123340661","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}
1. PROBLEM STATEMENT In interactive metaheuristic search, the human helps to steer the trajectory of search by providing qualitative evaluations of solution individuals in the population. Given that much metaheuristic search is typically population-based, it is challenging to design the presentation of solutions such that the human can provide effective qualitative evaluation [1]. Naively presenting each individual in a large population at each generation causes evaluation fatigue and a subsequent non-linearity of user focus making search trajectory inconsistent and ineffective [2].
{"title":"Metaheuristic design pattern: interactive solution presentation","authors":"M. Shackelford, C. Simons","doi":"10.1145/2598394.2609849","DOIUrl":"https://doi.org/10.1145/2598394.2609849","url":null,"abstract":"1. PROBLEM STATEMENT In interactive metaheuristic search, the human helps to steer the trajectory of search by providing qualitative evaluations of solution individuals in the population. Given that much metaheuristic search is typically population-based, it is challenging to design the presentation of solutions such that the human can provide effective qualitative evaluation [1]. Naively presenting each individual in a large population at each generation causes evaluation fatigue and a subsequent non-linearity of user focus making search trajectory inconsistent and ineffective [2].","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"30 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":"123623908","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 work presents a differential evolution algorithm for combinatorial optimization, in which a set-based representation and operators define subproblems that are used to explore the search space. The proposed method is tested on the capacitated centered clustering problem.
{"title":"Combinatorial optimization with differential evolution: a set-based approach","authors":"André L. Maravilha, J. A. Ramírez, F. Campelo","doi":"10.1145/2598394.2598463","DOIUrl":"https://doi.org/10.1145/2598394.2598463","url":null,"abstract":"This work presents a differential evolution algorithm for combinatorial optimization, in which a set-based representation and operators define subproblems that are used to explore the search space. The proposed method is tested on the capacitated centered clustering problem.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"123 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":"114231423","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}
Mohamed Wiem Mkaouer, M. Kessentini, Slim Bechikh, M. Cinnéide, K. Deb
Refactoring large systems involves several sources of uncertainty related to the severity levels of code smells to be corrected and the importance of the classes in which the smells are located. Due to the dynamic nature of software development, these values cannot be accurately determined in practice, leading to refactoring sequences that lack robustness. To address this problem, we introduced a multi-objective robust model, based on NSGA-II, for the software refactoring problem that tries to find the best trade-off between quality and robustness.
{"title":"Software refactoring under uncertainty: a robust multi-objective approach","authors":"Mohamed Wiem Mkaouer, M. Kessentini, Slim Bechikh, M. Cinnéide, K. Deb","doi":"10.1145/2598394.2598499","DOIUrl":"https://doi.org/10.1145/2598394.2598499","url":null,"abstract":"Refactoring large systems involves several sources of uncertainty related to the severity levels of code smells to be corrected and the importance of the classes in which the smells are located. Due to the dynamic nature of software development, these values cannot be accurately determined in practice, leading to refactoring sequences that lack robustness. To address this problem, we introduced a multi-objective robust model, based on NSGA-II, for the software refactoring problem that tries to find the best trade-off between quality and robustness.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"310 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":"115864548","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}
Neuronal signaling is one of several approaches to network nanomachines in the human body. This paper formulates a noisy optimization problem for a neuronal signaling protocol based on Time Division Multiple Access (TDMA) and solves the problem with a noise-aware optimizer that leverages an evolutionary algorithm. The proposed optimizer is intended to minimize signaling latency by multiplexing and parallelizing signal transmissions in a given neuronal network, while maximizing signaling robustness (i.e., unlikeliness of signal interference). Since latency and robustness objectives conflict with each other, the proposed optimizer seeks the optimal trade-offs between them. It exploits a nonparametric (i.e. distribution-free) statistical operator because it is not fully known what distribution(s) noise follows in each step/component in neuronal signaling. Simulation results show that the proposed optimizer efficiently obtains quality TDMA signaling schedules and operates a TDMA protocol by balancing conflicting objectives in noisy environments.
{"title":"Noise-aware evolutionary TDMA optimization for neuronal signaling in medical sensor-actuator networks","authors":"J. Suzuki, P. Boonma","doi":"10.1145/2598394.2609854","DOIUrl":"https://doi.org/10.1145/2598394.2609854","url":null,"abstract":"Neuronal signaling is one of several approaches to network nanomachines in the human body. This paper formulates a noisy optimization problem for a neuronal signaling protocol based on Time Division Multiple Access (TDMA) and solves the problem with a noise-aware optimizer that leverages an evolutionary algorithm. The proposed optimizer is intended to minimize signaling latency by multiplexing and parallelizing signal transmissions in a given neuronal network, while maximizing signaling robustness (i.e., unlikeliness of signal interference). Since latency and robustness objectives conflict with each other, the proposed optimizer seeks the optimal trade-offs between them. It exploits a nonparametric (i.e. distribution-free) statistical operator because it is not fully known what distribution(s) noise follows in each step/component in neuronal signaling. Simulation results show that the proposed optimizer efficiently obtains quality TDMA signaling schedules and operates a TDMA protocol by balancing conflicting objectives in noisy environments.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"18 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":"132133325","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}
Traffic assignment is a complex optimization problem. In case the road network has many links (thus a high number of alternative routes) and multiple origin-destination pairs, most existing solutions approximate the so-called user equilibrium (a variant of Nash equilibrium). Furthermore, the quality of these solutions (mostly, iterative algorithms) come at the expense of computational performance. In this study, we introduce a methodology to evaluate an approximation of an optimal traffic assignment from the global network's perspective based on genetic algorithms. This approach has been investigated in terms of both network performance (travel time) and convergence speed.
{"title":"Getting you faster to work: a genetic algorithm approach to the traffic assignment problem","authors":"Daniel Cagara, A. Bazzan, B. Scheuermann","doi":"10.1145/2598394.2598419","DOIUrl":"https://doi.org/10.1145/2598394.2598419","url":null,"abstract":"Traffic assignment is a complex optimization problem. In case the road network has many links (thus a high number of alternative routes) and multiple origin-destination pairs, most existing solutions approximate the so-called user equilibrium (a variant of Nash equilibrium). Furthermore, the quality of these solutions (mostly, iterative algorithms) come at the expense of computational performance. In this study, we introduce a methodology to evaluate an approximation of an optimal traffic assignment from the global network's perspective based on genetic algorithms. This approach has been investigated in terms of both network performance (travel time) and convergence speed.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"13 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":"132551750","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 quality of candidate solutions in evolutionary computation (EC) depend on multiple independent runs and a large number of them fail to guarantee optimal result. These runs consume more or less equal or sometimes higher amount of computational resources on par the runs that produce desirable results. This research work addresses these two issues (run quality, execution time), Run Prediction Model (RPM), in which undesirable quality evolutionary runs are identified to discontinue from their execution. An Ant Colony Optimization (ACO) based classifier that learns to discover a prediction model from the early generations of an EC run. We consider Grammatical Evolution (GE) as our EC technique to apply RPM that is evaluated on four symbolic regression problems. We establish that the RPM applied GE produces a significant improvement in the success rate while reducing the execution time.
{"title":"Predict the success or failure of an evolutionary algorithm run","authors":"Gopinath Chennupati, C. Ryan, R. Azad","doi":"10.1145/2598394.2598471","DOIUrl":"https://doi.org/10.1145/2598394.2598471","url":null,"abstract":"The quality of candidate solutions in evolutionary computation (EC) depend on multiple independent runs and a large number of them fail to guarantee optimal result. These runs consume more or less equal or sometimes higher amount of computational resources on par the runs that produce desirable results. This research work addresses these two issues (run quality, execution time), Run Prediction Model (RPM), in which undesirable quality evolutionary runs are identified to discontinue from their execution. An Ant Colony Optimization (ACO) based classifier that learns to discover a prediction model from the early generations of an EC run. We consider Grammatical Evolution (GE) as our EC technique to apply RPM that is evaluated on four symbolic regression problems. We establish that the RPM applied GE produces a significant improvement in the success rate while reducing the execution time.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"311 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":"133944244","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：