P. Nordin, F. Hoffmann, F. Francone, M. Brameier, W. Banzhaf
Many machine learning tasks are just too hard to be solved with a single processor machine, no matter how efficient the algorithms are and how fast our hardware is. Luckily genetic programming is well suited for parallelization compared to standard serial algorithms. The paper describes the first parallel implementation of an AIM-GP system, creating the potential for an extremely fast system. The system is tested on three problems and several variants of demes and migration are evaluated. Most of the results are applicable to both linear and tree based systems.
{"title":"AIM-GP and parallelism","authors":"P. Nordin, F. Hoffmann, F. Francone, M. Brameier, W. Banzhaf","doi":"10.1109/CEC.1999.782540","DOIUrl":"https://doi.org/10.1109/CEC.1999.782540","url":null,"abstract":"Many machine learning tasks are just too hard to be solved with a single processor machine, no matter how efficient the algorithms are and how fast our hardware is. Luckily genetic programming is well suited for parallelization compared to standard serial algorithms. The paper describes the first parallel implementation of an AIM-GP system, creating the potential for an extremely fast system. The system is tested on three problems and several variants of demes and migration are evaluated. Most of the results are applicable to both linear and tree based systems.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"107 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":"132598214","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 key idea behind cultural algorithms is to acquire problem solving knowledge (beliefs) from the evolving population and in return apply that knowledge to guide the search (R.G. Reynolds et al., 1993; 1996), In solving nonlinear constraint optimization problems, the key problem is how to represent and store the knowledge about the constraints. Previously, Chung (Chan-Jin Chung and R.G. Reynolds, 1996; 1998) used cultural algorithms to solve unconstraint optimization problems. Use was made of interval schemata proposed by L.J. Eshelman and J.D. Schaffer (1992) to represent global knowledge about the independent problem parameters. However, in constraint optimization, the problem intervals generally must be modified dependently. In order to solve constraint optimization problems, we need to extend the interval representation to allow for the representation of constraints. We define an n-dimensional regional based schema, called belief cell, which can provide an explicit mechanism to support the acquisition, storage and integration of knowledge about the constraints. In a cultural algorithm framework, the belief space can "contain" a set of these schemata, each of them can be used to guide the search of the evolving population, i.e. these kind of region based schemata can be used to guide the optimization search in a direct way by pruning the unfeasible regions and promoting the promising regions. We compared the results of 4 CA configurations that manipulate these schemata for an example problem.
{"title":"Using knowledge-based evolutionary computation to solve nonlinear constraint optimization problems: a cultural algorithm approach","authors":"Xidong Jin, R. Reynolds","doi":"10.1109/CEC.1999.785475","DOIUrl":"https://doi.org/10.1109/CEC.1999.785475","url":null,"abstract":"The key idea behind cultural algorithms is to acquire problem solving knowledge (beliefs) from the evolving population and in return apply that knowledge to guide the search (R.G. Reynolds et al., 1993; 1996), In solving nonlinear constraint optimization problems, the key problem is how to represent and store the knowledge about the constraints. Previously, Chung (Chan-Jin Chung and R.G. Reynolds, 1996; 1998) used cultural algorithms to solve unconstraint optimization problems. Use was made of interval schemata proposed by L.J. Eshelman and J.D. Schaffer (1992) to represent global knowledge about the independent problem parameters. However, in constraint optimization, the problem intervals generally must be modified dependently. In order to solve constraint optimization problems, we need to extend the interval representation to allow for the representation of constraints. We define an n-dimensional regional based schema, called belief cell, which can provide an explicit mechanism to support the acquisition, storage and integration of knowledge about the constraints. In a cultural algorithm framework, the belief space can \"contain\" a set of these schemata, each of them can be used to guide the search of the evolving population, i.e. these kind of region based schemata can be used to guide the optimization search in a direct way by pruning the unfeasible regions and promoting the promising regions. We compared the results of 4 CA configurations that manipulate these schemata for an example problem.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"14 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":"132953325","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 neural trees (ENTs) are tree-structured neural networks constructed by evolutionary algorithms. We use ENTs to build predictive models of time series data. Time series data are typically characterized by dynamics of the underlying process and thus the robustness of predictions is crucial. We describe a method for making more robust predictions by building committees of ENTs, i.e. CENTs. The method extends the concept of mixing genetic programming (MGP) which makes use of the fact that evolutionary computation produces multiple models as output instead of just one best. Experiments have been performed on the laser time series in which the CENTs outperformed the single best ENTs. We also discuss a theoretical foundation of CENTs using the Bayesian framework for evolutionary computation.
{"title":"Time series prediction using committee machines of evolutionary neural trees","authors":"Byoung-Tak Zhang, Je-Gun Joung","doi":"10.1109/CEC.1999.781937","DOIUrl":"https://doi.org/10.1109/CEC.1999.781937","url":null,"abstract":"Evolutionary neural trees (ENTs) are tree-structured neural networks constructed by evolutionary algorithms. We use ENTs to build predictive models of time series data. Time series data are typically characterized by dynamics of the underlying process and thus the robustness of predictions is crucial. We describe a method for making more robust predictions by building committees of ENTs, i.e. CENTs. The method extends the concept of mixing genetic programming (MGP) which makes use of the fact that evolutionary computation produces multiple models as output instead of just one best. Experiments have been performed on the laser time series in which the CENTs outperformed the single best ENTs. We also discuss a theoretical foundation of CENTs using the Bayesian framework for evolutionary computation.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"571 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":"133517995","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 an evolutionary search scheduling algorithm (ESSA) developed to solve the most difficult job shop scheduling problems (JSSP) that are known to be NP-hard combinatorial optimization problems. The ESSA proposed is a hybrid approach that focuses on optimization of locally optimized solutions. The differences versus other ESSA strategies are the new proposed encoding, decoding and forcing scheme, the local search optimizer that uses a new repair based neighborhood structure and a new bootstrapping strategy. Experimental results on common benchmarks indicate the power of the hybrid ESSA. The results clearly show that optimal schedules can be found. Moreover, the algorithm outperformed several ESSAs on average results with moderate computation time needed.
{"title":"A hybrid evolutionary search scheduling algorithm to solve the job shop scheduling problem","authors":"P. V. Bael, D. Devogelaere, M. Rijckaert","doi":"10.1109/CEC.1999.782546","DOIUrl":"https://doi.org/10.1109/CEC.1999.782546","url":null,"abstract":"This paper describes an evolutionary search scheduling algorithm (ESSA) developed to solve the most difficult job shop scheduling problems (JSSP) that are known to be NP-hard combinatorial optimization problems. The ESSA proposed is a hybrid approach that focuses on optimization of locally optimized solutions. The differences versus other ESSA strategies are the new proposed encoding, decoding and forcing scheme, the local search optimizer that uses a new repair based neighborhood structure and a new bootstrapping strategy. Experimental results on common benchmarks indicate the power of the hybrid ESSA. The results clearly show that optimal schedules can be found. Moreover, the algorithm outperformed several ESSAs on average results with moderate computation time needed.","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":"128859319","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 an extendable framework called "HENSON" that supports the development and application of genetic algorithm ("GA") visualizations. During the last few years the application of software visualization technology to support people's understanding and use of evolutionary computation ("EC") has been receiving increasing attention from within the EC community. However, the only visualization that could claim to be in common use is the "traditional" fitness versus time graph. It is suggested that the reason for the continuing lack of commonly used visualizations, is not due to a lack of good visualization design but rather a lack of good visualization support. In order for a visualization to be of practical use, the benefits of using the visualization must clearly outweigh the costs associated with producing it. Whilst the majority of EC visualization research continues to concentrate on the benefits of visualization, the work described in this paper concentrates on reducing the cost associated with producing visualizations. Thereby, improving the accessibility of visualization for GA users.
{"title":"HENSON: a visualization framework for genetic algorithm users","authors":"T. Collins","doi":"10.1109/CEC.1999.781983","DOIUrl":"https://doi.org/10.1109/CEC.1999.781983","url":null,"abstract":"This paper presents an extendable framework called \"HENSON\" that supports the development and application of genetic algorithm (\"GA\") visualizations. During the last few years the application of software visualization technology to support people's understanding and use of evolutionary computation (\"EC\") has been receiving increasing attention from within the EC community. However, the only visualization that could claim to be in common use is the \"traditional\" fitness versus time graph. It is suggested that the reason for the continuing lack of commonly used visualizations, is not due to a lack of good visualization design but rather a lack of good visualization support. In order for a visualization to be of practical use, the benefits of using the visualization must clearly outweigh the costs associated with producing it. Whilst the majority of EC visualization research continues to concentrate on the benefits of visualization, the work described in this paper concentrates on reducing the cost associated with producing visualizations. Thereby, improving the accessibility of visualization for GA users.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"30 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":"131318796","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 general, scheduling and sequencing problems are very difficult to solve to optimality (i.e., most problems are NP-Complete). In some instances, machines produce batch quantities of products which are placed in inventories. Demands are allocated directly from these inventories if available. If current inventory levels can not satisfy the demands and associated due dates, outsourcing some of the product, generally at a premium price offers a way to meet all due dates. Scheduling to meet due-dates coupled with inventory control is an important and more complex problem than the general scheduling problem. One application arises in furniture manufacturing where the lumber used to make furniture must first be dried from green lumber in a series of parallel batch machines (kilns). Drying lumber in-house is less expensive than purchasing commercially kiln-dried lumber. Therefore, the objective is to minimize the sum of the costs of drying lumber in-house and purchasing kiln-dried lumber in order to meet all due-dates plus any inventory carrying costs incurred over the planning horizon. The problem is decomposed into two sub problems: (1) the sequencing of the product types (lumber) on the machines (kilns); and (2) the allocation of inventory to satisfy the demands. A hybrid genetic algorithm determines the best sequence of product types to produce and an embedded linear program determines the optimal allocation of inventory and quantity of outsourced lumber that minimizes total cost. The hybrid algorithm is shown to be effective at solving the problem.
{"title":"Job sequencing and inventory control for a parallel machine problem: a hybrid-GA approach","authors":"J. Joines, C. Culbreth","doi":"10.1109/CEC.1999.782550","DOIUrl":"https://doi.org/10.1109/CEC.1999.782550","url":null,"abstract":"In general, scheduling and sequencing problems are very difficult to solve to optimality (i.e., most problems are NP-Complete). In some instances, machines produce batch quantities of products which are placed in inventories. Demands are allocated directly from these inventories if available. If current inventory levels can not satisfy the demands and associated due dates, outsourcing some of the product, generally at a premium price offers a way to meet all due dates. Scheduling to meet due-dates coupled with inventory control is an important and more complex problem than the general scheduling problem. One application arises in furniture manufacturing where the lumber used to make furniture must first be dried from green lumber in a series of parallel batch machines (kilns). Drying lumber in-house is less expensive than purchasing commercially kiln-dried lumber. Therefore, the objective is to minimize the sum of the costs of drying lumber in-house and purchasing kiln-dried lumber in order to meet all due-dates plus any inventory carrying costs incurred over the planning horizon. The problem is decomposed into two sub problems: (1) the sequencing of the product types (lumber) on the machines (kilns); and (2) the allocation of inventory to satisfy the demands. A hybrid genetic algorithm determines the best sequence of product types to produce and an embedded linear program determines the optimal allocation of inventory and quantity of outsourced lumber that minimizes total cost. The hybrid algorithm is shown to be effective at solving the problem.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"33 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":"131895961","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 the direct solution of sparse symmetric and positive definite linear systems, finding an ordering of the matrix to minimize the height of elimination tree (an indication of the number of parallel elimination steps) is crucial for effectively computing the Cholesky factor in parallel. This problem is known to be NP-hard. Though many effective heuristics have been proposed, the problems of how good these heuristics are near optimal and how to further reduce the height of elimination tree remain unanswered. This paper is an effort to this investigation. We introduce a genetic algorithm customized to this parallel ordering problem, which is characterized by two novel genetic operators, adaptive merge crossover and tree rotate mutation. Experiments showed that our approach is cost effective in the number of generations evolved to reach a better solution that having considerable improvement in reducing the height of elimination tree.
{"title":"Parallel sparse matrix ordering: quality improvement using genetic algorithms","authors":"Wen-Yang Lin","doi":"10.1109/CEC.1999.785560","DOIUrl":"https://doi.org/10.1109/CEC.1999.785560","url":null,"abstract":"In the direct solution of sparse symmetric and positive definite linear systems, finding an ordering of the matrix to minimize the height of elimination tree (an indication of the number of parallel elimination steps) is crucial for effectively computing the Cholesky factor in parallel. This problem is known to be NP-hard. Though many effective heuristics have been proposed, the problems of how good these heuristics are near optimal and how to further reduce the height of elimination tree remain unanswered. This paper is an effort to this investigation. We introduce a genetic algorithm customized to this parallel ordering problem, which is characterized by two novel genetic operators, adaptive merge crossover and tree rotate mutation. Experiments showed that our approach is cost effective in the number of generations evolved to reach a better solution that having considerable improvement in reducing the height of elimination tree.","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":"127405663","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}
Increasing attention is being paid to various problems inherent in the topological design of network systems. The topological structure of these networks can be based on service centers, terminals (users), and connection cables. Lately, these network systems have been designed with fiber optic cable, due to increasing user requirements. But considering the high cost of the fiber optic cable, it is desirable that the network architecture is composed of a spanning tree. Network topology design problems consist of finding a topology that optimizes design criteria such as connection cost, message delay, network reliability, and so on. Recently, genetic algorithms (GAs) have advanced in many research fields, such as network optimization problems, combinatorial optimization, multi-objective optimization, and so on. Also, GAs have received a great deal of attention concerning their ability as an optimization technique for many real-world problems. In this paper, a GA for solving bicriteria network topology design problems of wide-band communication networks connected with fiber optic cable is presented, considering network reliability. We also employ the Prufer number and cluster string in order to represent chromosomes. Finally, we present some experiments in order to certify the quality of the network designs obtained by using the proposed GA. From the results, the proposed method can search effectively better candidate network architecture.
{"title":"Genetic algorithm for solving bicriteria network topology design problem","authors":"Jong Ryul Kim, M. Gen","doi":"10.1109/CEC.1999.785557","DOIUrl":"https://doi.org/10.1109/CEC.1999.785557","url":null,"abstract":"Increasing attention is being paid to various problems inherent in the topological design of network systems. The topological structure of these networks can be based on service centers, terminals (users), and connection cables. Lately, these network systems have been designed with fiber optic cable, due to increasing user requirements. But considering the high cost of the fiber optic cable, it is desirable that the network architecture is composed of a spanning tree. Network topology design problems consist of finding a topology that optimizes design criteria such as connection cost, message delay, network reliability, and so on. Recently, genetic algorithms (GAs) have advanced in many research fields, such as network optimization problems, combinatorial optimization, multi-objective optimization, and so on. Also, GAs have received a great deal of attention concerning their ability as an optimization technique for many real-world problems. In this paper, a GA for solving bicriteria network topology design problems of wide-band communication networks connected with fiber optic cable is presented, considering network reliability. We also employ the Prufer number and cluster string in order to represent chromosomes. Finally, we present some experiments in order to certify the quality of the network designs obtained by using the proposed GA. From the results, the proposed method can search effectively better candidate network architecture.","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":"115668720","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}
Truly autonomous vehicles will require both projective planning and reactive components in order to perform robustly. Projective components are needed for long term planning and re-planning where explicit reasoning about future states is required. Reactive components allow the system to always have some action available in real time, and themselves can exhibit robust behaviour, but lack the ability to explicitly reason about future states over a long time period. The paper emphasises creating the projective component but also offer a simple solution for reactive component. A genetic algorithm implements the projective component, which designs automatically a fuzzy logic controller by modifying the position of controller membership functions and the commands given to the robot. For the reactive component, a simple solution was adopted so that if the robot sensors detect new obstacles, the robot will try to move to a previous position.
{"title":"Control of autonomous robots using fuzzy logic controllers tuned by genetic algorithms","authors":"Corneliu T. C. Arsene, A. Zalzala","doi":"10.1109/CEC.1999.781956","DOIUrl":"https://doi.org/10.1109/CEC.1999.781956","url":null,"abstract":"Truly autonomous vehicles will require both projective planning and reactive components in order to perform robustly. Projective components are needed for long term planning and re-planning where explicit reasoning about future states is required. Reactive components allow the system to always have some action available in real time, and themselves can exhibit robust behaviour, but lack the ability to explicitly reason about future states over a long time period. The paper emphasises creating the projective component but also offer a simple solution for reactive component. A genetic algorithm implements the projective component, which designs automatically a fuzzy logic controller by modifying the position of controller membership functions and the commands given to the robot. For the reactive component, a simple solution was adopted so that if the robot sensors detect new obstacles, the robot will try to move to a previous position.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"134 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":"114374207","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}
D. B. Fogel, P. Angeline, V. W. Porto, E. C. Wasson, E. Boughton
Computer assisted mammography can be used to provide a second opinion and may improve the sensitivity and specificity of diagnosis. Algorithms may also provide a basis for mining data from available training sets, thereby allowing the user to recognize relationships between input features and alternative conditions (e.g., malignant, benign). The paper provides a review of recent efforts to evolve neural networks and linear classifiers to assist in the detection of breast cancer.
{"title":"Using evolutionary computation to learn about detecting breast cancer","authors":"D. B. Fogel, P. Angeline, V. W. Porto, E. C. Wasson, E. Boughton","doi":"10.1109/CEC.1999.785485","DOIUrl":"https://doi.org/10.1109/CEC.1999.785485","url":null,"abstract":"Computer assisted mammography can be used to provide a second opinion and may improve the sensitivity and specificity of diagnosis. Algorithms may also provide a basis for mining data from available training sets, thereby allowing the user to recognize relationships between input features and alternative conditions (e.g., malignant, benign). The paper provides a review of recent efforts to evolve neural networks and linear classifiers to assist in the detection of breast cancer.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"28 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":"114634229","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：