A. Stoica, Gerhard Klimeck, C. Salazar-Lazaro, D. Keymeulen, A. Thakoor
The paper addresses the use of evolutionary algorithms in the design of electronic devices and circuits. In particular, the paper introduces the idea of evolutionary design of nanodevices, and illustrates it with the design of a resonant tunneling diode. A second experiment, this time using CMOS microdevices, illustrates the use of evolutionary algorithms for circuit design. The experiments were facilitated by an evolutionary design tool developed around a parallel implementation of genetic algorithms (using PGAPack), and device/circuit simulators (NEMO and SPICE). It is speculated that in the future, devices and circuits may be simultaneously co-designed.
{"title":"Evolutionary design of electronic devices and circuits","authors":"A. Stoica, Gerhard Klimeck, C. Salazar-Lazaro, D. Keymeulen, A. Thakoor","doi":"10.1109/CEC.1999.782588","DOIUrl":"https://doi.org/10.1109/CEC.1999.782588","url":null,"abstract":"The paper addresses the use of evolutionary algorithms in the design of electronic devices and circuits. In particular, the paper introduces the idea of evolutionary design of nanodevices, and illustrates it with the design of a resonant tunneling diode. A second experiment, this time using CMOS microdevices, illustrates the use of evolutionary algorithms for circuit design. The experiments were facilitated by an evolutionary design tool developed around a parallel implementation of genetic algorithms (using PGAPack), and device/circuit simulators (NEMO and SPICE). It is speculated that in the future, devices and circuits may be simultaneously co-designed.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130382621","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}
A Genetic Programming algorithm using discrete Fourier transforms is used to evolve an automatic object detector of vehicles for infrared images. Results show promise for the solution of a real world problem.
{"title":"Object detection by multiple textural analyzers","authors":"D. Howard, S. C. Roberts","doi":"10.1109/CEC.1999.782511","DOIUrl":"https://doi.org/10.1109/CEC.1999.782511","url":null,"abstract":"A Genetic Programming algorithm using discrete Fourier transforms is used to evolve an automatic object detector of vehicles for infrared images. Results show promise for the solution of a real world problem.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129344999","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}
Job rotation is one method that is sometimes used to reduce exposure to strenuous material handling, however, developing effective rotation schedules can be complex in even moderate size facilities. The purpose of this research is to develop methods of incorporating safety criteria into scheduling algorithms to produce job rotation schedules that reduce the potential for injury. Integer programming and a genetic algorithm were used to construct job rotation schedules. Schedules were comprised of lifting tasks whose potential for causing injury was assessed with the Job Severity Index. Each method was used to design four job rotation schedules that met specified safety criteria in a working environment where the object weight, horizontal distance, and repetition rate varied over time. Each rotation was assigned to a specific gender/lifting capacity group. The advantages and limitations of these approaches in developing administrative controls for the prevention of back injury are discussed.
{"title":"A genetic algorithm for designing job rotation schedules considering ergonomic constraints","authors":"B. Carnahan, M. Redfern, B. Norman","doi":"10.1109/CEC.1999.782544","DOIUrl":"https://doi.org/10.1109/CEC.1999.782544","url":null,"abstract":"Job rotation is one method that is sometimes used to reduce exposure to strenuous material handling, however, developing effective rotation schedules can be complex in even moderate size facilities. The purpose of this research is to develop methods of incorporating safety criteria into scheduling algorithms to produce job rotation schedules that reduce the potential for injury. Integer programming and a genetic algorithm were used to construct job rotation schedules. Schedules were comprised of lifting tasks whose potential for causing injury was assessed with the Job Severity Index. Each method was used to design four job rotation schedules that met specified safety criteria in a working environment where the object weight, horizontal distance, and repetition rate varied over time. Each rotation was assigned to a specific gender/lifting capacity group. The advantages and limitations of these approaches in developing administrative controls for the prevention of back injury are discussed.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130806480","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 deals with identical parallel machine scheduling problems with two kinds of objective functions, i.e., both regular and non-regular objective functions, and proposes a genetic algorithm approach in which (a) the sequence of jobs on each machine as well as the assignment of jobs to machines are determined directly by referring to a string (genotype), and (b) the start time of each job is fixed by solving the linear programming problem and a feasible schedule (phenotype) is obtained. As for (b), we newly introduce a method of representing the problem to determine the start time of each job as a linear programming problem whose objective function is formed as a weighted sum of the original multiple objective functions. This method enables us to obtain a lot of potential schedules. Moreover, through computational experiments by using our genetic algorithm approach, the effectiveness for generating a variety of Pareto-optimal schedules is investigated.
{"title":"A genetic algorithm approach to multi-objective scheduling problems with earliness and tardiness penalties","authors":"H. Tamaki, Etsuo Nishino, S. Abe","doi":"10.1109/CEC.1999.781906","DOIUrl":"https://doi.org/10.1109/CEC.1999.781906","url":null,"abstract":"This paper deals with identical parallel machine scheduling problems with two kinds of objective functions, i.e., both regular and non-regular objective functions, and proposes a genetic algorithm approach in which (a) the sequence of jobs on each machine as well as the assignment of jobs to machines are determined directly by referring to a string (genotype), and (b) the start time of each job is fixed by solving the linear programming problem and a feasible schedule (phenotype) is obtained. As for (b), we newly introduce a method of representing the problem to determine the start time of each job as a linear programming problem whose objective function is formed as a weighted sum of the original multiple objective functions. This method enables us to obtain a lot of potential schedules. Moreover, through computational experiments by using our genetic algorithm approach, the effectiveness for generating a variety of Pareto-optimal schedules is investigated.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124513936","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}
Jorng-Tzong Horng, Yu-Jan Chang, Baw-Jhiune Liu, Cheng-Yan Kao
A data warehouse stores lots of materialized views to provide efficient decision-support or OLAP queries. The view-selection problem addresses the selection of a fittest set of materialized views under the limitation of storage space forms a challenge in data warehouse research. In this paper, we present genetic algorithms to choose materialized views. We also use experiments to demonstrate the power of our approach.
{"title":"Materialized view selection using genetic algorithms in a data warehouse system","authors":"Jorng-Tzong Horng, Yu-Jan Chang, Baw-Jhiune Liu, Cheng-Yan Kao","doi":"10.1109/CEC.1999.785551","DOIUrl":"https://doi.org/10.1109/CEC.1999.785551","url":null,"abstract":"A data warehouse stores lots of materialized views to provide efficient decision-support or OLAP queries. The view-selection problem addresses the selection of a fittest set of materialized views under the limitation of storage space forms a challenge in data warehouse research. In this paper, we present genetic algorithms to choose materialized views. We also use experiments to demonstrate the power of our approach.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128977915","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}
A genetic algorithm is applied to the problem of conditioning the petrophysical rock properties of a reservoir model on historic production data. This is a difficult optimization problem where each evaluation of the objective function implies a flow simulation of the whole reservoir. Due to the high computing cost of this function, it is imperative to make use of an efficient optimization method to find a near optimal solution using as few iterations as possible. We have applied a genetic algorithm to this problem. Ten independent runs are used to give a prediction with an uncertainty estimate for the total future oil production using two different production strategies.
{"title":"Oil reservoir production forecasting with uncertainty estimation using genetic algorithms","authors":"H. Soleng","doi":"10.1109/CEC.1999.782574","DOIUrl":"https://doi.org/10.1109/CEC.1999.782574","url":null,"abstract":"A genetic algorithm is applied to the problem of conditioning the petrophysical rock properties of a reservoir model on historic production data. This is a difficult optimization problem where each evaluation of the objective function implies a flow simulation of the whole reservoir. Due to the high computing cost of this function, it is imperative to make use of an efficient optimization method to find a near optimal solution using as few iterations as possible. We have applied a genetic algorithm to this problem. Ten independent runs are used to give a prediction with an uncertainty estimate for the total future oil production using two different production strategies.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114374369","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}
Evolutionary programming typically uses tournament selection to choose parents for reproduction. Tournaments naturally emphasize survival. However, a natural opposite of survival is extinction, and a study of the fossil record shows extinction plays a key role in the evolutionary process. This paper presents a new evolutionary algorithm that emphasizes extinction to conduct search operations over a fitness landscape.
{"title":"Emphasizing extinction in evolutionary programming","authors":"G. W. Greewood, G. Fogel, M. Ciobanu","doi":"10.1109/CEC.1999.781997","DOIUrl":"https://doi.org/10.1109/CEC.1999.781997","url":null,"abstract":"Evolutionary programming typically uses tournament selection to choose parents for reproduction. Tournaments naturally emphasize survival. However, a natural opposite of survival is extinction, and a study of the fossil record shows extinction plays a key role in the evolutionary process. This paper presents a new evolutionary algorithm that emphasizes extinction to conduct search operations over a fitness landscape.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122509089","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}
An evolutionary algorithm (EA) approach is used to generate test vectors for the detection of shrinkage faults in programmable logic arrays (PLA). Three basic steps are performed during the generation of the test vectors: crossover, mutation and selection. A new mutation operator is introduced that helps increase the Hamming distance among the candidate solutions. Once crossover and mutation have occurred, the new candidate test vectors with higher fitness function scores replace the old ones. With this scheme, population members steadily improve their fitness level with each new generation. The resulting process yields improved solutions to the problem of the PLA test vector generation for shrinkage faults. PLA testing and fault simulation is computationally prohibitive in uniprocessor machines. However, PLAGA is well suited for powerful parallel processing machines with vectorization capability,.
{"title":"PLAGA: a highly parallelizable genetic algorithm for programmable logic arrays test pattern generation","authors":"Alfiedo Cruz, S. Mukherjee","doi":"10.1109/CEC.1999.782524","DOIUrl":"https://doi.org/10.1109/CEC.1999.782524","url":null,"abstract":"An evolutionary algorithm (EA) approach is used to generate test vectors for the detection of shrinkage faults in programmable logic arrays (PLA). Three basic steps are performed during the generation of the test vectors: crossover, mutation and selection. A new mutation operator is introduced that helps increase the Hamming distance among the candidate solutions. Once crossover and mutation have occurred, the new candidate test vectors with higher fitness function scores replace the old ones. With this scheme, population members steadily improve their fitness level with each new generation. The resulting process yields improved solutions to the problem of the PLA test vector generation for shrinkage faults. PLA testing and fault simulation is computationally prohibitive in uniprocessor machines. However, PLAGA is well suited for powerful parallel processing machines with vectorization capability,.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121070810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
When we search for an infinitely large number of solutions by evolutionary algorithms, it is helpful to learn the topology of the fitness landscape to know whether the solutions we obtained are representative samples of the whole solutions. Some solutions are easy to be approached and others are not in general. As a step to learn the whole geometry of fitness landscape, we exploit, in this paper, a downhill walk by evolutionary programming to reveal the shape of global peaks on the fitness landscape defined on weight space.
{"title":"Downhill walk from the top of a hill by evolutionary programming","authors":"A. Imada","doi":"10.1109/CEC.1999.782648","DOIUrl":"https://doi.org/10.1109/CEC.1999.782648","url":null,"abstract":"When we search for an infinitely large number of solutions by evolutionary algorithms, it is helpful to learn the topology of the fitness landscape to know whether the solutions we obtained are representative samples of the whole solutions. Some solutions are easy to be approached and others are not in general. As a step to learn the whole geometry of fitness landscape, we exploit, in this paper, a downhill walk by evolutionary programming to reveal the shape of global peaks on the fitness landscape defined on weight space.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126928401","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 body of theoretical results regarding conservation of information ("no free lunch") in optimization has not related directly to evolutionary computation. Prior work has assumed that an optimizer traverses a sequence of points in the domain of a function without revisiting points. The present work reduces the difference between theory and practice by a) allowing points to be revisited, b) reasoning about the set of visited points instead of the sequence, and c) considering the impact of bounded memory and revisited points upon optimizer performance. Fortuitously, this leads to clarification of the fundamental results in conservation of information. Although most work in this area emphasizes the futility of attempting to design a generally superior optimizer, the present work highlights possible constructive use of the theory in restricted problem domains.
{"title":"Some information theoretic results on evolutionary optimization","authors":"T. M. English","doi":"10.1109/CEC.1999.782013","DOIUrl":"https://doi.org/10.1109/CEC.1999.782013","url":null,"abstract":"The body of theoretical results regarding conservation of information (\"no free lunch\") in optimization has not related directly to evolutionary computation. Prior work has assumed that an optimizer traverses a sequence of points in the domain of a function without revisiting points. The present work reduces the difference between theory and practice by a) allowing points to be revisited, b) reasoning about the set of visited points instead of the sequence, and c) considering the impact of bounded memory and revisited points upon optimizer performance. Fortuitously, this leads to clarification of the fundamental results in conservation of information. Although most work in this area emphasizes the futility of attempting to design a generally superior optimizer, the present work highlights possible constructive use of the theory in restricted problem domains.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131259324","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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