This paper proposes a novel evolutionary computing method called a genetic quantum algorithm (GQA). GQA is based on the concept and principles of quantum computing such as qubits and superposition of states. Instead of binary, numeric, or symbolic representation, by adopting qubit chromosome as a representation GQA can represent a linear superposition of solutions due to its probabilistic representation. As genetic operators, quantum gates are employed for the search of the best solution. Rapid convergence and good global search capability characterize the performance of GQA. The effectiveness and the applicability of GQA are demonstrated by experimental results on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that GQA is superior to other genetic algorithms using penalty functions, repair methods and decoders.
{"title":"Genetic quantum algorithm and its application to combinatorial optimization problem","authors":"Kuk-Hyun Han, Jong-Hwan Kim","doi":"10.1109/CEC.2000.870809","DOIUrl":"https://doi.org/10.1109/CEC.2000.870809","url":null,"abstract":"This paper proposes a novel evolutionary computing method called a genetic quantum algorithm (GQA). GQA is based on the concept and principles of quantum computing such as qubits and superposition of states. Instead of binary, numeric, or symbolic representation, by adopting qubit chromosome as a representation GQA can represent a linear superposition of solutions due to its probabilistic representation. As genetic operators, quantum gates are employed for the search of the best solution. Rapid convergence and good global search capability characterize the performance of GQA. The effectiveness and the applicability of GQA are demonstrated by experimental results on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that GQA is superior to other genetic algorithms using penalty functions, repair methods and decoders.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115501304","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. Collings, A. Reeder, Iqbal Adjali, P. Crocker, M. Lyons
Understanding the rate of adoption of a telecommunications service in a population of customers is of prime importance to ensure that appropriate network capacity is provided to maintain quality of service. This problem goes beyond assessing the demand for a product based on usage and requires an understanding of how consumers learn about a service and evaluate its worth. Field studies have shown that word of mouth recommendations and knowledge of a service have a significant impact on adoption rates. Adopters of the Internet can be influenced through communications at work or children learning at school. The authors present an agent based model of a population of customers, with rules based on field data, which is being used to understand how services are adopted. Of particular interest is how customers interact to learn about the service through their communications with other customers. We show how the different structure, dynamics and distribution of the social networks affect the diffusion of a service through a customer population. Our model shows that real world adoption rates are a combination of these mechanisms which interact in a non-linear and complex manner. This complex systems approach provides a useful way to decompose these interactions.
{"title":"Agent based customer modelling: individuals who learn from their environment","authors":"D. Collings, A. Reeder, Iqbal Adjali, P. Crocker, M. Lyons","doi":"10.1109/CEC.2000.870830","DOIUrl":"https://doi.org/10.1109/CEC.2000.870830","url":null,"abstract":"Understanding the rate of adoption of a telecommunications service in a population of customers is of prime importance to ensure that appropriate network capacity is provided to maintain quality of service. This problem goes beyond assessing the demand for a product based on usage and requires an understanding of how consumers learn about a service and evaluate its worth. Field studies have shown that word of mouth recommendations and knowledge of a service have a significant impact on adoption rates. Adopters of the Internet can be influenced through communications at work or children learning at school. The authors present an agent based model of a population of customers, with rules based on field data, which is being used to understand how services are adopted. Of particular interest is how customers interact to learn about the service through their communications with other customers. We show how the different structure, dynamics and distribution of the social networks affect the diffusion of a service through a customer population. Our model shows that real world adoption rates are a combination of these mechanisms which interact in a non-linear and complex manner. This complex systems approach provides a useful way to decompose these interactions.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115557119","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 flow shop sequencing model (FSSM) that incorporates actual constraints encountered in practice is proposed for the difficult case of specialized precast production scheduling. The model is solved using a genetic algorithm (GA). The traditional minimize makespan and the more practical minimize tardiness penalty objective functions are optimized separately, as well as simultaneously using a weighted approach. Experiments are conducted to investigate the effect of increasing population size and seeding the initial population with heuristic solutions. Comparisons between the GA and classical heuristic rules show that the GA is competitive, if not better than heuristic rules in discovering a set of good solutions.
{"title":"Precast production scheduling with genetic algorithms","authors":"W. Chan, H. Hu","doi":"10.1109/CEC.2000.870768","DOIUrl":"https://doi.org/10.1109/CEC.2000.870768","url":null,"abstract":"A flow shop sequencing model (FSSM) that incorporates actual constraints encountered in practice is proposed for the difficult case of specialized precast production scheduling. The model is solved using a genetic algorithm (GA). The traditional minimize makespan and the more practical minimize tardiness penalty objective functions are optimized separately, as well as simultaneously using a weighted approach. Experiments are conducted to investigate the effect of increasing population size and seeding the initial population with heuristic solutions. Comparisons between the GA and classical heuristic rules show that the GA is competitive, if not better than heuristic rules in discovering a set of good solutions.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114551829","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 article generalizes a previously presented dynamic fitness function with two different concepts, namely a coordinate rotation and the concept of partial visibility. Those concepts define different classes of test problems. A set of standard evolution strategies and genetic algorithms with and without hypermutation are tested on two of the dynamic problem classes. They give insight into certain properties of the presented concepts and dynamic optimization in general.
{"title":"Dynamic rotation and partial visibility","authors":"Karsten Weicker, N. Weicker","doi":"10.1109/CEC.2000.870774","DOIUrl":"https://doi.org/10.1109/CEC.2000.870774","url":null,"abstract":"This article generalizes a previously presented dynamic fitness function with two different concepts, namely a coordinate rotation and the concept of partial visibility. Those concepts define different classes of test problems. A set of standard evolution strategies and genetic algorithms with and without hypermutation are tested on two of the dynamic problem classes. They give insight into certain properties of the presented concepts and dynamic optimization in general.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114654874","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 the application and performance evaluation of a new algorithm for multiple objective optimization problems (MOOP) based on reinforcement learning. The new algorithm, called MDQL, considers a family of agents for each objective function involved in a MOOP. Each agent proposes a solution for its corresponding objective function. Agents leave traces while they construct solutions considering traces made by other agents. The solutions proposed by the agents are evaluated using a non-domination criterion and solutions in the final Pareto set for each iteration are rewarded. A mechanism for the application of MDQL in continuous spaces which considers a fixed set of possible actions for the states (the number of actions depends on the dimensionality of the MOOP), is also proposed. Each action represents a path direction and its magnitude is changed dynamically depending on the evaluation of the state that the agent reached. Constraint handling, based on reinforcement comparison, considers reference values for constraints, penalizing agents violating any of them proportionally to the violation committed. MDQL performance was measured with "error ratio" and "spacing" metrics on four test bed problems suggested in the literature, showing competitive results with state-of-the-art algorithms.
{"title":"Distributed reinforcement learning for multiple objective optimization problems","authors":"C. Mariano, E. Morales","doi":"10.1109/CEC.2000.870294","DOIUrl":"https://doi.org/10.1109/CEC.2000.870294","url":null,"abstract":"This paper describes the application and performance evaluation of a new algorithm for multiple objective optimization problems (MOOP) based on reinforcement learning. The new algorithm, called MDQL, considers a family of agents for each objective function involved in a MOOP. Each agent proposes a solution for its corresponding objective function. Agents leave traces while they construct solutions considering traces made by other agents. The solutions proposed by the agents are evaluated using a non-domination criterion and solutions in the final Pareto set for each iteration are rewarded. A mechanism for the application of MDQL in continuous spaces which considers a fixed set of possible actions for the states (the number of actions depends on the dimensionality of the MOOP), is also proposed. Each action represents a path direction and its magnitude is changed dynamically depending on the evaluation of the state that the agent reached. Constraint handling, based on reinforcement comparison, considers reference values for constraints, penalizing agents violating any of them proportionally to the violation committed. MDQL performance was measured with \"error ratio\" and \"spacing\" metrics on four test bed problems suggested in the literature, showing competitive results with state-of-the-art algorithms.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117107226","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 discuss how many satisfiable solutions a genetic algorithm can find in a problem instance of a constraint satisfaction problems in a single execution. Hence, we propose a framework for a new fitness function which can be applied to traditional fitness functions. However, the mechanism of the proposed fitness function is quite simple, and several experimental results on a variety of instances of general constraint satisfaction problems demonstrate the effectiveness of the proposed fitness function.
{"title":"A new fitness function for discovering a lot of satisfiable solutions in constraint satisfaction problems","authors":"H. Handa, O. Katai, T. Konishi, Mitsuru Baba","doi":"10.1109/CEC.2000.870783","DOIUrl":"https://doi.org/10.1109/CEC.2000.870783","url":null,"abstract":"In this paper, we discuss how many satisfiable solutions a genetic algorithm can find in a problem instance of a constraint satisfaction problems in a single execution. Hence, we propose a framework for a new fitness function which can be applied to traditional fitness functions. However, the mechanism of the proposed fitness function is quite simple, and several experimental results on a variety of instances of general constraint satisfaction problems demonstrate the effectiveness of the proposed fitness function.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116381205","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 underlying dynamics of algal species in freshwater systems are a complex non-linear problem. Process-based models have been previously developed to describe the time varying behaviour of chlorophyll-a, a measure of algal concentration, for these systems. This paper describes the application of a genetic programming equation discovery system to study various generalisations of a process-based model based on a time series difference equation.
{"title":"Evolving difference equations to model freshwater phytoplankton","authors":"P. Whigham, F. Recknagel","doi":"10.1109/CEC.2000.870748","DOIUrl":"https://doi.org/10.1109/CEC.2000.870748","url":null,"abstract":"The underlying dynamics of algal species in freshwater systems are a complex non-linear problem. Process-based models have been previously developed to describe the time varying behaviour of chlorophyll-a, a measure of algal concentration, for these systems. This paper describes the application of a genetic programming equation discovery system to study various generalisations of a process-based model based on a time series difference equation.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123205596","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}
Can we realise the opportunities that lie in design by evolution by using traditional technologies or are there better technologies which will allow us to fully realise the potential inherent in evolvable hardware? The authors consider the characteristics of evolvable hardware, especially for adaptive design, and discuss the demands that these characteristics place on the underlying technology. They suggest a potential alternative to today's FPGA technology. The proposed architecture is particularly focused at reducing the genotype required for a given design by reducing the configuration data required for unused routing resources and allowing partial configuration down to a single CLB. In addition, to support adaptive hardware, self-reconfiguration is enabled.
{"title":"An evolvable hardware FPGA for adaptive hardware","authors":"P. Haddow, G. Tufte","doi":"10.1109/CEC.2000.870345","DOIUrl":"https://doi.org/10.1109/CEC.2000.870345","url":null,"abstract":"Can we realise the opportunities that lie in design by evolution by using traditional technologies or are there better technologies which will allow us to fully realise the potential inherent in evolvable hardware? The authors consider the characteristics of evolvable hardware, especially for adaptive design, and discuss the demands that these characteristics place on the underlying technology. They suggest a potential alternative to today's FPGA technology. The proposed architecture is particularly focused at reducing the genotype required for a given design by reducing the configuration data required for unused routing resources and allowing partial configuration down to a single CLB. In addition, to support adaptive hardware, self-reconfiguration is enabled.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122068293","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 setting of parameters in Evolutionary Algorithms (EA) has crucial influence on their performance. Typically, the best choice depends on the optimization task. Some parameters yield better results when they are varied during the run. Recently, the so-called Terrain-Based Genetic Algorithm (TBGA) was introduced, which is a self-tuning version of the traditional Cellular Genetic Algorithm (CGA). In a TBGA, the individuals of the population are placed in a two-dimensional grid, where only neighbored individuals can mate with each other. The position of an individual in this grid is interpreted as its offspring's specific mutation rate and number of crossover points. This approach allows to apply GA parameters that are optimal for (i) the type of optimization task and (ii) the current state of the optimization process. However, only a few individuals can apply the optimal parameters simultaneously due to their fixed position in the grid lattice. In this paper, we substituted the fixed spatial structure of CGAs with the agent-based Patchwork model. In this model individuals can move between neighbored grid cells, and the number of individuals per grid cell is variable but limited. With this design, several individuals were able to use beneficial parameters simultaneously and to follow optimal parameter settings over time. Our new approach achieved better results than our original Patchwork model and the TBGA.
{"title":"Parameter control using the agent based patchwork model","authors":"T. Krink, R. K. Ursem","doi":"10.1109/CEC.2000.870278","DOIUrl":"https://doi.org/10.1109/CEC.2000.870278","url":null,"abstract":"The setting of parameters in Evolutionary Algorithms (EA) has crucial influence on their performance. Typically, the best choice depends on the optimization task. Some parameters yield better results when they are varied during the run. Recently, the so-called Terrain-Based Genetic Algorithm (TBGA) was introduced, which is a self-tuning version of the traditional Cellular Genetic Algorithm (CGA). In a TBGA, the individuals of the population are placed in a two-dimensional grid, where only neighbored individuals can mate with each other. The position of an individual in this grid is interpreted as its offspring's specific mutation rate and number of crossover points. This approach allows to apply GA parameters that are optimal for (i) the type of optimization task and (ii) the current state of the optimization process. However, only a few individuals can apply the optimal parameters simultaneously due to their fixed position in the grid lattice. In this paper, we substituted the fixed spatial structure of CGAs with the agent-based Patchwork model. In this model individuals can move between neighbored grid cells, and the number of individuals per grid cell is variable but limited. With this design, several individuals were able to use beneficial parameters simultaneously and to follow optimal parameter settings over time. Our new approach achieved better results than our original Patchwork model and the TBGA.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122070624","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}
Both stochastic learning automata and genetic algorithms have previously been shown to have valuable global optimisation properties. Learning automata have however been criticised for their perceived slow rate of convergence. In this paper these two techniques are combined to provide an increase in the rate of convergence for the learning automata and also to improve the escape from local minima. The technique separates the genotype and phenotype properties of the genetic algorithm and has the advantage that the degree of convergence can be quickly ascertained. It also provides the genetic algorithm with a stopping rule and enables bounds to be given on the parameter values obtained.
{"title":"On the genetic adaptation of stochastic learning automata","authors":"Mark N Howell, T. Gordon","doi":"10.1109/CEC.2000.870767","DOIUrl":"https://doi.org/10.1109/CEC.2000.870767","url":null,"abstract":"Both stochastic learning automata and genetic algorithms have previously been shown to have valuable global optimisation properties. Learning automata have however been criticised for their perceived slow rate of convergence. In this paper these two techniques are combined to provide an increase in the rate of convergence for the learning automata and also to improve the escape from local minima. The technique separates the genotype and phenotype properties of the genetic algorithm and has the advantage that the degree of convergence can be quickly ascertained. It also provides the genetic algorithm with a stopping rule and enables bounds to be given on the parameter values obtained.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117185695","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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