In natural systems, many animals organize into groups without a designated leader and still perform complex collective behaviors. Although individuals in the group may be considered equal, all the individuals differ in the traits each of them possess. Of particular interest is the idea of an individual's personality as it often plays a role in determining which individuals lead collective behaviors. Personality is, in part, developed and maintained by an individual's experiences. However, neither an individual, nor its environment remains unchanged. Therefore, there is a need for an individual to continue to gain new experiences to ensure that its information about itself and its environment are current. Since observations have shown that the effects of experience on personality can decay over time, we investigate the effects of this decay on the emergence of leaders and followers and the resulting success of a group's collective movement attempts. Results show that personality decay has a negative effect on the overall success of the group in collective movements as it prevents the emergence of distinct personalities, a necessary requirement for individuals to assume distinct leader and follower roles.
{"title":"Effects of personality decay on collective movements","authors":"Jeremy Acre, Nicholas Zoller, B. E. Eskridge","doi":"10.1145/2598394.2605678","DOIUrl":"https://doi.org/10.1145/2598394.2605678","url":null,"abstract":"In natural systems, many animals organize into groups without a designated leader and still perform complex collective behaviors. Although individuals in the group may be considered equal, all the individuals differ in the traits each of them possess. Of particular interest is the idea of an individual's personality as it often plays a role in determining which individuals lead collective behaviors. Personality is, in part, developed and maintained by an individual's experiences. However, neither an individual, nor its environment remains unchanged. Therefore, there is a need for an individual to continue to gain new experiences to ensure that its information about itself and its environment are current. Since observations have shown that the effects of experience on personality can decay over time, we investigate the effects of this decay on the emergence of leaders and followers and the resulting success of a group's collective movement attempts. Results show that personality decay has a negative effect on the overall success of the group in collective movements as it prevents the emergence of distinct personalities, a necessary requirement for individuals to assume distinct leader and follower roles.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"56 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":"128238870","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}
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage, and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). Copyright is held by the author/owner(s). GECCO ’14, Jul 12-16 2014, Vancouver, BC, Canada ACM 978-1-4503-2881-4/14/07. http://dx.doi.org/10.1145/2598394.2605365 Part I
{"title":"Automatic (offline) configuration of algorithms","authors":"Manuel López-Ibáñez, T. Stützle","doi":"10.1145/2598394.2605365","DOIUrl":"https://doi.org/10.1145/2598394.2605365","url":null,"abstract":"Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage, and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). Copyright is held by the author/owner(s). GECCO ’14, Jul 12-16 2014, Vancouver, BC, Canada ACM 978-1-4503-2881-4/14/07. http://dx.doi.org/10.1145/2598394.2605365 Part I","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"56 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":"129482668","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 describes a method of solving the symbolic regression problem using developmental linear genetic programming (DLGP) with an epigenetic hill climber (EHC). We propose the EHC for optimizing the epigenetic properties of the genotype. The epigenetic characteristics are then inherited through coevolution with the population. Results reveal that the EHC improves performance through maintenance of smaller expressed program sizes. For some problems it produces more successful runs while remaining essentially cost-neutral with respect to number of fitness evaluations.
{"title":"Evolving differential equations with developmental linear genetic programming and epigenetic hill climbing","authors":"W. L. Cava, L. Spector, K. Danai, M. Lackner","doi":"10.1145/2598394.2598491","DOIUrl":"https://doi.org/10.1145/2598394.2598491","url":null,"abstract":"This paper describes a method of solving the symbolic regression problem using developmental linear genetic programming (DLGP) with an epigenetic hill climber (EHC). We propose the EHC for optimizing the epigenetic properties of the genotype. The epigenetic characteristics are then inherited through coevolution with the population. Results reveal that the EHC improves performance through maintenance of smaller expressed program sizes. For some problems it produces more successful runs while remaining essentially cost-neutral with respect to number of fitness evaluations.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"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":"130308909","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 helper-objective approach for solving the job-shop scheduling problem using multi-objective evolutionary algorithms is considered. We implemented the approach from the Lochtefeld and Ciarallo paper using NSGA-II with the correct implementation of the non-dominated sorting procedure which is able to work with equal values of objectives. The experimental evaluation showed the significant improvement of solution quality. We also report new best results for 16 out of 24 problem instances used in the considered paper.
{"title":"NSGA-II implementation details may influence quality of solutions for the job-shop scheduling problem","authors":"M. Buzdalov, Irina Petrova, Arina Buzdalova","doi":"10.1145/2598394.2602288","DOIUrl":"https://doi.org/10.1145/2598394.2602288","url":null,"abstract":"The helper-objective approach for solving the job-shop scheduling problem using multi-objective evolutionary algorithms is considered. We implemented the approach from the Lochtefeld and Ciarallo paper using NSGA-II with the correct implementation of the non-dominated sorting procedure which is able to work with equal values of objectives. The experimental evaluation showed the significant improvement of solution quality. We also report new best results for 16 out of 24 problem instances used in the considered paper.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"11 2 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":"128876880","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 address the problem of constructing correct-by-design programs with the use of the automata-based programming paradigm. A recent algorithm for learning finite-state machines (FSMs) MuACOsm is applied to the problem of inferring extended finite-state machine (EFSM) models from behavior examples (test scenarios) and temporal properties, and is shown to outperform the genetic algorithm (GA) used earlier.
{"title":"Inferring automata-based programs from specification with mutation-based ant colony optimization","authors":"D. Chivilikhin, V. Ulyantsev","doi":"10.1145/2598394.2598446","DOIUrl":"https://doi.org/10.1145/2598394.2598446","url":null,"abstract":"In this paper we address the problem of constructing correct-by-design programs with the use of the automata-based programming paradigm. A recent algorithm for learning finite-state machines (FSMs) MuACOsm is applied to the problem of inferring extended finite-state machine (EFSM) models from behavior examples (test scenarios) and temporal properties, and is shown to outperform the genetic algorithm (GA) used earlier.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"64 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":"132853109","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}
Image reconstruction from projections in tomography is an ill-posed, inverse problem. This problem is always a challenge because there are no standard methods which give satisfactory results. In this paper we propose hybridization between Local Search (LS) and Harmony Search (HS) metaheuristics to improve quality of reconstructed images. The proposed method is implemented, tested on some images and compared to LS and Filtered backprojection (FBP) methods. The preliminary results are promising and prove the efficiency of our method.
{"title":"Improving reconstructed images using hybridization between local search and harmony search meta-heuristics","authors":"A. Ouaddah, D. Boughaci","doi":"10.1145/2598394.2602283","DOIUrl":"https://doi.org/10.1145/2598394.2602283","url":null,"abstract":"Image reconstruction from projections in tomography is an ill-posed, inverse problem. This problem is always a challenge because there are no standard methods which give satisfactory results. In this paper we propose hybridization between Local Search (LS) and Harmony Search (HS) metaheuristics to improve quality of reconstructed images. The proposed method is implemented, tested on some images and compared to LS and Filtered backprojection (FBP) methods. The preliminary results are promising and prove the efficiency of our method.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"34 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":"133346740","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 extraction of weak signals from instrumental noise is a critical task in ongoing searches for gravitational waves. A detection and estimation method, made feasible by Particle Swarm Optimization, is presented for a particularly challenging class of signals expected from astrophysical sources.
{"title":"Detection and estimation of unmodeled narrowband nonstationary signals: application of particle swarm optimization in gravitational wave data analysis","authors":"S. Mohanty","doi":"10.1145/2598394.2598439","DOIUrl":"https://doi.org/10.1145/2598394.2598439","url":null,"abstract":"The extraction of weak signals from instrumental noise is a critical task in ongoing searches for gravitational waves. A detection and estimation method, made feasible by Particle Swarm Optimization, is presented for a particularly challenging class of signals expected from astrophysical sources.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"20 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":"114502528","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 metaheuristic algorithms applied to certain problems, it may be difficult to design search operators that guarantee producing feasible search points. In such cases, it may be more efficient to allow a search operator to yield an infeasible solution, and then turn it into a feasible one using a repair process. This paper is an attempt to provide a broad perspective on the candidate solution repair and frame it as a metaheuristic design pattern.
{"title":"Metaheuristic design pattern: candidate solution repair","authors":"K. Krawiec","doi":"10.1145/2598394.2609847","DOIUrl":"https://doi.org/10.1145/2598394.2609847","url":null,"abstract":"In metaheuristic algorithms applied to certain problems, it may be difficult to design search operators that guarantee producing feasible search points. In such cases, it may be more efficient to allow a search operator to yield an infeasible solution, and then turn it into a feasible one using a repair process. This paper is an attempt to provide a broad perspective on the candidate solution repair and frame it as a metaheuristic design pattern.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"12 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":"131973967","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 book on Evolutionary Image Analysis and Signal Processing, besides celebrating ten years of EvoIASP, the only event specifically dedicated to this topic since 1999, offers readers a panoramic view of what can be presently achieved using Evolutionary Computation techniques in computer vision, pattern recognition, and image and signal processing. Its chapters mostly consist of extended versions of a selection of papers which were presented at recent editions of EvoIASP. The book includes examples which span, rather uniformly, the whole range of roles Evolutionary Computation techniques may have in such applications, from representing optimization tools used to tune or refine parameters or components of a mostly predefined solution up to situations where the solution itself is intrinsically evolutionary.
{"title":"Evolutionary image analysis and signal processing","authors":"S. Cagnoni","doi":"10.1145/2598394.2605359","DOIUrl":"https://doi.org/10.1145/2598394.2605359","url":null,"abstract":"This book on Evolutionary Image Analysis and Signal Processing, besides celebrating ten years of EvoIASP, the only event specifically dedicated to this topic since 1999, offers readers a panoramic view of what can be presently achieved using Evolutionary Computation techniques in computer vision, pattern recognition, and image and signal processing. Its chapters mostly consist of extended versions of a selection of papers which were presented at recent editions of EvoIASP. The book includes examples which span, rather uniformly, the whole range of roles Evolutionary Computation techniques may have in such applications, from representing optimization tools used to tune or refine parameters or components of a mostly predefined solution up to situations where the solution itself is intrinsically evolutionary.","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":"132799849","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}
Data mining techniques enable efficient extraction of useful knowledge from a large data repository. However, it also can disclose sensitive information if used inappropriately. A feasible way to address this problem is to sanitize the database to conceal sensitive information. In this paper, we focus on privacy preserving in association rule mining. In light of the tradeoff between hiding sensitive rules and disclosing non-sensitive ones during the hiding process, a novel association rule hiding approach is proposed based on evolutionary multi-objective optimization (EMO). It modifies the original database by deleting identified transactions/tuples to hide sensitive rules. Experiment results are reported to show the effectiveness of the proposed approach.
{"title":"Completely hide sensitive association rules using EMO by deleting transactions","authors":"Peng Cheng, Jeng-Shyang Pan","doi":"10.1145/2598394.2598466","DOIUrl":"https://doi.org/10.1145/2598394.2598466","url":null,"abstract":"Data mining techniques enable efficient extraction of useful knowledge from a large data repository. However, it also can disclose sensitive information if used inappropriately. A feasible way to address this problem is to sanitize the database to conceal sensitive information. In this paper, we focus on privacy preserving in association rule mining. In light of the tradeoff between hiding sensitive rules and disclosing non-sensitive ones during the hiding process, a novel association rule hiding approach is proposed based on evolutionary multi-objective optimization (EMO). It modifies the original database by deleting identified transactions/tuples to hide sensitive rules. Experiment results are reported to show the effectiveness of the proposed approach.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"43 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":"133113782","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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