Xin-yuan Zhang, Yue-jiao Gong, Jingjing Li, Ying Lin
Scheduling the operating mode of nodes is an effective way to maximize the lifetime of wireless sensor networks (WSN). For a WSN with randomly and densely deployed sensors, we could maximize the lifetime of WSN through finding the maximum number of disjoint complete cover sets. Most of the related work focuses on 2D ideal plane. However, deploying sensors on the 3D surface is more practical in real world scenarios. We propose a novel genetic algorithm with redundant sensor auto-adjustment, termed RSAGA. In order to adapt the original GA into this application, we employ some effective mechanisms along with the basic crossover, mutation, and selection operation. The proposed operator of redundant sensor auto-adjustment schedules the redundant sensors in complete cover sets into incomplete cover sets so as to improve the coverage of the latters. A rearrangement operation specially designed for the critical sensors is embedded in the mutation operator to fine-tune the node arrangement of critical fields. Moreover, we modify the traditional cost function by increasing the penalty of incomplete cover sets for improving the convergence rate of finding feasible solutions. Simulation has been conducted to evaluate the performance of RSAGA. The experimental results show that the proposed RSAGA possesses very promising performance in terms of solution quality and robustness.
{"title":"Evolutionary computation for lifetime maximization of wireless sensor networks in complex 3D environments","authors":"Xin-yuan Zhang, Yue-jiao Gong, Jingjing Li, Ying Lin","doi":"10.1145/2598394.2598415","DOIUrl":"https://doi.org/10.1145/2598394.2598415","url":null,"abstract":"Scheduling the operating mode of nodes is an effective way to maximize the lifetime of wireless sensor networks (WSN). For a WSN with randomly and densely deployed sensors, we could maximize the lifetime of WSN through finding the maximum number of disjoint complete cover sets. Most of the related work focuses on 2D ideal plane. However, deploying sensors on the 3D surface is more practical in real world scenarios. We propose a novel genetic algorithm with redundant sensor auto-adjustment, termed RSAGA. In order to adapt the original GA into this application, we employ some effective mechanisms along with the basic crossover, mutation, and selection operation. The proposed operator of redundant sensor auto-adjustment schedules the redundant sensors in complete cover sets into incomplete cover sets so as to improve the coverage of the latters. A rearrangement operation specially designed for the critical sensors is embedded in the mutation operator to fine-tune the node arrangement of critical fields. Moreover, we modify the traditional cost function by increasing the penalty of incomplete cover sets for improving the convergence rate of finding feasible solutions. Simulation has been conducted to evaluate the performance of RSAGA. The experimental results show that the proposed RSAGA possesses very promising performance in terms of solution quality and robustness.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"119 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":"116714305","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 paper proposed a new genetic clustering algorithm with variable-length chromosome representation (GCVCR), which can automatically evolve and find the optimal number of clusters as well as proper cluster centers of the data set. A new clustering criterion based on message passing between data points and the candidate centers described by the chromosome are presented to make the clustering problem more effective. The simulation results show the effectiveness of the proposed algorithm.
{"title":"A novel genetic clustering algorithm with variable-length chromosome representation","authors":"Ming-an Zhang, Yong Deng, Dong-xia Chang","doi":"10.1145/2598394.2602272","DOIUrl":"https://doi.org/10.1145/2598394.2602272","url":null,"abstract":"The paper proposed a new genetic clustering algorithm with variable-length chromosome representation (GCVCR), which can automatically evolve and find the optimal number of clusters as well as proper cluster centers of the data set. A new clustering criterion based on message passing between data points and the candidate centers described by the chromosome are presented to make the clustering problem more effective. The simulation results show the effectiveness of the proposed algorithm.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"23 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":"116666589","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 fast genetic algorithm (GA) for solving the flexible job shob scheduling problem (FJSP). The FJSP is an extension of a classical NP-hard job shop scheduling problem. Here, we combine the active schedule constructive crossover (ASCX) with the generalized order crossover (GOX). Also, we show how to divide a population of solutions in the high-low fit selection scheme in order to guide the search efficiently. An initial experimental study indicates high convergence capabilities of the proposed GA.
{"title":"A fast genetic algorithm for the flexible job shop scheduling problem","authors":"Marcin Cwiek, J. Nalepa","doi":"10.1145/2598394.2602280","DOIUrl":"https://doi.org/10.1145/2598394.2602280","url":null,"abstract":"This paper presents a fast genetic algorithm (GA) for solving the flexible job shob scheduling problem (FJSP). The FJSP is an extension of a classical NP-hard job shop scheduling problem. Here, we combine the active schedule constructive crossover (ASCX) with the generalized order crossover (GOX). Also, we show how to divide a population of solutions in the high-low fit selection scheme in order to guide the search efficiently. An initial experimental study indicates high convergence capabilities of the proposed GA.","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":"115828822","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}
Fate Agent EAs form a novel flavour or subclass in EC. The idea is to decompose the main loop of traditional evolutionary algorithms into three independently acting forces, implemented by the so-called Fate Agents, and create an evolutionary process by injecting these agents into a population of candidate solutions. This paper introduces an extension to the original concept, adding a mechanism to self-adapt the mutation of the Breeder Agents. The method improves the behaviour of the original Fate Agent EA on dynamically changing fitness landscapes.
{"title":"Fate agent evolutionary algorithms with self-adaptive mutation","authors":"Arthur-Ervin Avramiea, G. Karafotias, A. Eiben","doi":"10.1145/2598394.2598497","DOIUrl":"https://doi.org/10.1145/2598394.2598497","url":null,"abstract":"Fate Agent EAs form a novel flavour or subclass in EC. The idea is to decompose the main loop of traditional evolutionary algorithms into three independently acting forces, implemented by the so-called Fate Agents, and create an evolutionary process by injecting these agents into a population of candidate solutions. This paper introduces an extension to the original concept, adding a mechanism to self-adapt the mutation of the Breeder Agents. The method improves the behaviour of the original Fate Agent EA on dynamically changing fitness landscapes.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"48 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":"128164329","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 our work on Estimation of Distribution Algorithms (EDAs) that address sequencing problems, i.e., the task of finding the best ordering of a set of items or an optimal schedule to perform a given set of operations. Specifically, we focus on using probabilistic models based on $n$-gram statistics. These models have been used extensively in modeling the statistical properties of sequences. We start with an EDA that uses a bigram model, then extend this scheme to higher-order models. However, directly replacing the bigram model with a higher-order model results in premature convergence. We give an explanation on this situation, along with some empirical support. We then introduce a technique for combining multiple models of different orders, which allows for smooth transition from lower-order models to higher-order ones. Furthermore, this technique can also be used to incorporate other heuristics as well as prior knowledge about the problem into the search process. Promising preliminary results on solving Traveling Salesman Problems (TSPs) are presented.
{"title":"Estimation of distribution algorithms based on n-gramstatistics for sequencing and optimization","authors":"C. Chuang, Stephen F. Smith","doi":"10.1145/2598394.2598399","DOIUrl":"https://doi.org/10.1145/2598394.2598399","url":null,"abstract":"This paper presents our work on Estimation of Distribution Algorithms (EDAs) that address sequencing problems, i.e., the task of finding the best ordering of a set of items or an optimal schedule to perform a given set of operations. Specifically, we focus on using probabilistic models based on $n$-gram statistics. These models have been used extensively in modeling the statistical properties of sequences. We start with an EDA that uses a bigram model, then extend this scheme to higher-order models. However, directly replacing the bigram model with a higher-order model results in premature convergence. We give an explanation on this situation, along with some empirical support. We then introduce a technique for combining multiple models of different orders, which allows for smooth transition from lower-order models to higher-order ones. Furthermore, this technique can also be used to incorporate other heuristics as well as prior knowledge about the problem into the search process. Promising preliminary results on solving Traveling Salesman Problems (TSPs) are presented.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"39 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":"126421725","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 report we describe our research involving the construction of quantum-based robotic controllers. By careful use of quantum interference as a computational resource and by utilizing only a linear number of elementary unitary transformations, we are able to construct systems which seem to provide a computational advantage even when simulated on a classical computer.
{"title":"Minimal variable quantum decision makers for robotic control","authors":"Walter O. Krawec","doi":"10.1145/2598394.2598409","DOIUrl":"https://doi.org/10.1145/2598394.2598409","url":null,"abstract":"In this report we describe our research involving the construction of quantum-based robotic controllers. By careful use of quantum interference as a computational resource and by utilizing only a linear number of elementary unitary transformations, we are able to construct systems which seem to provide a computational advantage even when simulated on a classical computer.","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":"121921291","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, July 12-16, 2014, Vancouver, BC, Canada. ACM 978-1-4503-2881-4/14/07. http://dx.doi.org/10.1145/2598394.2605363
{"title":"Introduction to evolutionary game theory","authors":"Marco Tomassini","doi":"10.1145/2598394.2605363","DOIUrl":"https://doi.org/10.1145/2598394.2605363","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, July 12-16, 2014, Vancouver, BC, Canada. ACM 978-1-4503-2881-4/14/07. http://dx.doi.org/10.1145/2598394.2605363","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"185 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":"121728642","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}
Designing genetic regulatory networks (GRNs) to achieve a desired cellular function is one of the main goals of synthetic biology. However, determining minimal GRNs that produce desired time-series behaviors is non-trivial. In this paper, we propose a 'top-down' approach, wherein we start with relatively dense GRNs and then use differential evolution (DE) to evolve interaction coefficients. When the target dynamical behavior is found embedded in a dense GRN, we narrow the focus of the search and begin aggressively pruning out excess interactions at the end of each generation. We first show that the method can quickly rediscover known small GRNs for a toggle switch and an oscillatory circuit. Next we include these GRNs as non-evolvable subnetworks in the subsequent evolution of more complex, modular GRNs. By incorporating aggressive pruning and a penalty term, the DE was able to find minimal or nearly minimal GRNs in all test problems.
{"title":"Evolving small GRNs with a top-down approach","authors":"Javier Garcia-Bernardo, M. Eppstein","doi":"10.1145/2598394.2598443","DOIUrl":"https://doi.org/10.1145/2598394.2598443","url":null,"abstract":"Designing genetic regulatory networks (GRNs) to achieve a desired cellular function is one of the main goals of synthetic biology. However, determining minimal GRNs that produce desired time-series behaviors is non-trivial. In this paper, we propose a 'top-down' approach, wherein we start with relatively dense GRNs and then use differential evolution (DE) to evolve interaction coefficients. When the target dynamical behavior is found embedded in a dense GRN, we narrow the focus of the search and begin aggressively pruning out excess interactions at the end of each generation. We first show that the method can quickly rediscover known small GRNs for a toggle switch and an oscillatory circuit. Next we include these GRNs as non-evolvable subnetworks in the subsequent evolution of more complex, modular GRNs. By incorporating aggressive pruning and a penalty term, the DE was able to find minimal or nearly minimal GRNs in all test problems.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"22 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":"125015937","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. J. M. Guervós, Pedro Ángel Castillo Valdivieso, A. García, Anna I. Esparcia-Alcázar, Víctor Manuel Rivas Santos
After more than fifteen years, JavaScript has finally risen as a popular language for implementing all kind of applications, from server-based to rich internet applications. The fact that it is implemented in the browser and in server-side tools makes it interesting for designing evolutionary algorithm frameworks that encompass both tiers, but besides, they allow a change in paradigm that goes beyond the canonical evolutionary algorithm. In this paper we will experiment with different architectures, client-server and peer to peer to assess which ones offer most advantages in terms of performance, scalability and ease of use for the computer scientist. All implementations have been released as open source, and besides showing that the concept of working with evolutionary algorithms in JavaScript can be done efficiently, we prove that a master-slave parallel architecture offers the best combination of time and algorithmic improvements in a parallel evolutionary algorithm that leverages JavaScript implementation features.
{"title":"NodEO, a multi-paradigm distributed evolutionary algorithm platform in JavaScript","authors":"J. J. M. Guervós, Pedro Ángel Castillo Valdivieso, A. García, Anna I. Esparcia-Alcázar, Víctor Manuel Rivas Santos","doi":"10.1145/2598394.2605688","DOIUrl":"https://doi.org/10.1145/2598394.2605688","url":null,"abstract":"After more than fifteen years, JavaScript has finally risen as a popular language for implementing all kind of applications, from server-based to rich internet applications. The fact that it is implemented in the browser and in server-side tools makes it interesting for designing evolutionary algorithm frameworks that encompass both tiers, but besides, they allow a change in paradigm that goes beyond the canonical evolutionary algorithm. In this paper we will experiment with different architectures, client-server and peer to peer to assess which ones offer most advantages in terms of performance, scalability and ease of use for the computer scientist. All implementations have been released as open source, and besides showing that the concept of working with evolutionary algorithms in JavaScript can be done efficiently, we prove that a master-slave parallel architecture offers the best combination of time and algorithmic improvements in a parallel evolutionary algorithm that leverages JavaScript implementation features.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"16 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":"122090957","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}
Luis A. Alvarado-Yañez, L. Torres-Treviño, F. Gonzalez, L. Nieves
Improvement of processes in metallurgical industry is a constant of competitive enterprises, however, changes made in a process are risky and involves high cost and time, considering this, a model can be made even using inputs usually not presented in real process and its analysis could be useful for the improvement of the process. In this work, a mathematical model is built using only experimental data of a four high tandem cold rolling mill, a set of input variables involving characteristics of the process. The performance of the model is determined by residual analysis considering new data. Results are a non black box model with a good performance; by this way, the model is a good representation of the process under study.
{"title":"A mathematical model of a cold rolling mill by symbolic regression alpha-beta","authors":"Luis A. Alvarado-Yañez, L. Torres-Treviño, F. Gonzalez, L. Nieves","doi":"10.1145/2598394.2609858","DOIUrl":"https://doi.org/10.1145/2598394.2609858","url":null,"abstract":"Improvement of processes in metallurgical industry is a constant of competitive enterprises, however, changes made in a process are risky and involves high cost and time, considering this, a model can be made even using inputs usually not presented in real process and its analysis could be useful for the improvement of the process. In this work, a mathematical model is built using only experimental data of a four high tandem cold rolling mill, a set of input variables involving characteristics of the process. The performance of the model is determined by residual analysis considering new data. Results are a non black box model with a good performance; by this way, the model is a good representation of the process under study.","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":"123336266","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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