Lambda calculus representation of programs offers a more expressive alternative to traditional S-expressions. In this paper we discuss advantages of this representation coming from the use of reductions (beta and eta) and a way to overcome disadvantages caused by variables occurring in the programs by use of the abstraction elimination algorithm. We discuss the role of those reductions in the process of generating initial population and propose two novel crossover operations based on abstraction elimination capable of handling general form of typed lambda term while being a straight generalization of the standard crossover operation. We compare their performances using the even parity benchmark problem.
{"title":"Utilization of reductions and abstraction elimination in typed genetic programming","authors":"Tomás Kren, Roman Neruda","doi":"10.1145/2576768.2598361","DOIUrl":"https://doi.org/10.1145/2576768.2598361","url":null,"abstract":"Lambda calculus representation of programs offers a more expressive alternative to traditional S-expressions. In this paper we discuss advantages of this representation coming from the use of reductions (beta and eta) and a way to overcome disadvantages caused by variables occurring in the programs by use of the abstraction elimination algorithm. We discuss the role of those reductions in the process of generating initial population and propose two novel crossover operations based on abstraction elimination capable of handling general form of typed lambda term while being a straight generalization of the standard crossover operation. We compare their performances using the even parity benchmark problem.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"42 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":"115322385","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 robotics often involves optimization in large, complex search spaces, requiring good population diversity. Recently, measures to actively increase diversity or novelty have been employed in order to get sufficient exploration of the search space either as the sole optimization objective or in combination with some performance measurement. When evolving morphology in addition to the control system, it can be difficult to construct a measure that sufficiently captures the qualitative differences between individuals. In this paper we investigate four diversity measures, applied in a set of evolutionary robotics experiments using an indirect encoding for evolving robot morphology. In the experiments we optimize forward locomotion capabilities of symmetrical legged robots in a physics simulation. Two distance measures in Cartesian phenotype feature spaces are compared with two methods operating in the space of possible morphology graphs. These measures are used for computing a diversity objective in a multi-objective evolutionary algorithm, and compared to a control case with no diversity objective. For the given task one of the distance measures shows a clear improvement over the control case in improving the main objectives, while others display better ability to diversify, underlining the difficulty of designing good, general measures of morphological diversity.
{"title":"Some distance measures for morphological diversification in generative evolutionary robotics","authors":"Eivind Samuelsen, K. Glette","doi":"10.1145/2576768.2598325","DOIUrl":"https://doi.org/10.1145/2576768.2598325","url":null,"abstract":"Evolutionary robotics often involves optimization in large, complex search spaces, requiring good population diversity. Recently, measures to actively increase diversity or novelty have been employed in order to get sufficient exploration of the search space either as the sole optimization objective or in combination with some performance measurement. When evolving morphology in addition to the control system, it can be difficult to construct a measure that sufficiently captures the qualitative differences between individuals. In this paper we investigate four diversity measures, applied in a set of evolutionary robotics experiments using an indirect encoding for evolving robot morphology. In the experiments we optimize forward locomotion capabilities of symmetrical legged robots in a physics simulation. Two distance measures in Cartesian phenotype feature spaces are compared with two methods operating in the space of possible morphology graphs. These measures are used for computing a diversity objective in a multi-objective evolutionary algorithm, and compared to a control case with no diversity objective. For the given task one of the distance measures shows a clear improvement over the control case in improving the main objectives, while others display better ability to diversify, underlining the difficulty of designing good, general measures of morphological diversity.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"50 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":"123293180","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}
Shaukat Ali, Muhammad Zohaib Z. Iqbal, Andrea Arcuri
The Object Constraint Language (OCL) is a standard language for specifying constraints on Unified Modeling Language (UML) models. The specified constraints can be used for various purposes including verification, and model-based testing (e.g., test data generation). Efficiently solving OCL constraints is one of the key requirements for the practical use of OCL. In this paper, we propose an improvement in existing heuristics to solve OCL constraints using search algorithms. We evaluate our improved heuristics using two empirical studies with three search algorithms: Alternating Variable Method (AVM), (1+1) Evolutionary Algorithm (EA), and a Genetic Algorithm (GA). We also used Random Search (RS) as a comparison baseline. The first empirical study was conducted using carefully designed artificial problems (constraints) to assess each individual heuristics. The second empirical study is based on an industrial case study provided by Cisco about model-based testing of Video Conferencing Systems. The results of both empirical evaluations reveal that the effectiveness of the search algorithms, measured in terms of time to solve the OCL constraints to generate data, is significantly improved when using the novel heuristics presented in this paper. In particular, our experiments show that (1+1) EA with the novel heuristics has the highest success rate among all the analyzed algorithms, as it requires the least number of iterations to solve constraints.
{"title":"Improved heuristics for solving OCL constraints using search algorithms","authors":"Shaukat Ali, Muhammad Zohaib Z. Iqbal, Andrea Arcuri","doi":"10.1145/2576768.2598308","DOIUrl":"https://doi.org/10.1145/2576768.2598308","url":null,"abstract":"The Object Constraint Language (OCL) is a standard language for specifying constraints on Unified Modeling Language (UML) models. The specified constraints can be used for various purposes including verification, and model-based testing (e.g., test data generation). Efficiently solving OCL constraints is one of the key requirements for the practical use of OCL. In this paper, we propose an improvement in existing heuristics to solve OCL constraints using search algorithms. We evaluate our improved heuristics using two empirical studies with three search algorithms: Alternating Variable Method (AVM), (1+1) Evolutionary Algorithm (EA), and a Genetic Algorithm (GA). We also used Random Search (RS) as a comparison baseline. The first empirical study was conducted using carefully designed artificial problems (constraints) to assess each individual heuristics. The second empirical study is based on an industrial case study provided by Cisco about model-based testing of Video Conferencing Systems. The results of both empirical evaluations reveal that the effectiveness of the search algorithms, measured in terms of time to solve the OCL constraints to generate data, is significantly improved when using the novel heuristics presented in this paper. In particular, our experiments show that (1+1) EA with the novel heuristics has the highest success rate among all the analyzed algorithms, as it requires the least number of iterations to solve constraints.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"213 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":"121718671","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}
Estimation of Distribution Algorithms (EDAs) have emerged from the synergy between machine-learning techniques and Genetic Algorithms (GAs). EDAs rely on probabilistic modeling for obtaining information about the underlying structure of optimization problems and implementing effective reproduction operators. The effectiveness of EDAs depends on the capacity of the model-building to extract reliable information about the problem. In this study we analyze additively separable functions and argue that the degree of multimodality of such functions defines their linkage-learning difficulty. Besides, by using entropy-based concepts and Jensen's inequality, we show how allelic pairwise independence may appear as a consequence of an increasing multimodality. The results characterize the linkage-learning difficulty of well-known functions, like the deceptive trap, bipolar and concatenated parity.
{"title":"Multimodality and the linkage-learning difficulty of additively separable functions","authors":"J. P. Martins, A. Delbem","doi":"10.1145/2576768.2598281","DOIUrl":"https://doi.org/10.1145/2576768.2598281","url":null,"abstract":"Estimation of Distribution Algorithms (EDAs) have emerged from the synergy between machine-learning techniques and Genetic Algorithms (GAs). EDAs rely on probabilistic modeling for obtaining information about the underlying structure of optimization problems and implementing effective reproduction operators. The effectiveness of EDAs depends on the capacity of the model-building to extract reliable information about the problem. In this study we analyze additively separable functions and argue that the degree of multimodality of such functions defines their linkage-learning difficulty. Besides, by using entropy-based concepts and Jensen's inequality, we show how allelic pairwise independence may appear as a consequence of an increasing multimodality. The results characterize the linkage-learning difficulty of well-known functions, like the deceptive trap, bipolar and concatenated parity.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"6 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":"125322749","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}
Justin K. Pugh, Skyler Goodell, Kenneth O. Stanley
The question of how to best design a communication architecture is becoming increasingly important for evolving autonomous multiagent systems. Directional reception of signals, a design feature of communication that appears in most animals, is present in only some existing artificial communication systems. This paper hypothesizes that such directional reception benefits the evolution of communicating autonomous agents because it simplifies the language required to express positional information, which is critical to solving many group coordination tasks. This hypothesis is tested by comparing the evolutionary performance of several alternative communication architectures (both directional and non-directional) in a multiagent foraging domain designed to require a basic "come here" type of signal for the optimal solution. Results confirm that directional reception is a key ingredient in the evolutionary tractability of effective communication. Furthermore, the real world viability of directional reception is demonstrated through the successful transfer of the best evolved controllers to real robots. The conclusion is that directional reception is important to consider when designing communication architectures for more complicated tasks in the future.
{"title":"Directional communication in evolved multiagent teams","authors":"Justin K. Pugh, Skyler Goodell, Kenneth O. Stanley","doi":"10.1145/2576768.2598299","DOIUrl":"https://doi.org/10.1145/2576768.2598299","url":null,"abstract":"The question of how to best design a communication architecture is becoming increasingly important for evolving autonomous multiagent systems. Directional reception of signals, a design feature of communication that appears in most animals, is present in only some existing artificial communication systems. This paper hypothesizes that such directional reception benefits the evolution of communicating autonomous agents because it simplifies the language required to express positional information, which is critical to solving many group coordination tasks. This hypothesis is tested by comparing the evolutionary performance of several alternative communication architectures (both directional and non-directional) in a multiagent foraging domain designed to require a basic \"come here\" type of signal for the optimal solution. Results confirm that directional reception is a key ingredient in the evolutionary tractability of effective communication. Furthermore, the real world viability of directional reception is demonstrated through the successful transfer of the best evolved controllers to real robots. The conclusion is that directional reception is important to consider when designing communication architectures for more complicated tasks in the future.","PeriodicalId":123241,"journal":{"name":"Proceedings 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":"129285505","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. Efstathiou, P. McBurney, S. Zschaler, Johann Bourcier
Infrastructure-less mobile ad-hoc networks enable the development of collaborative pervasive applications. Within such dynamic networks, collaboration between devices can be realised through service-orientation by abstracting device resources as services. Recently, a framework for QoS-aware service composition has been introduced which takes into account a spectrum of orchestration patterns, and enables compositions of a better QoS than traditional centralised orchestration approaches. In this paper, we focus on the automated exploration of trade-off compositions within the search space defined by this flexible composition model. For the studied problem, the evaluation of the fitness functions guiding the search process is computationally expensive because it either involves a high-fidelity simulation or actually requires calling the composite service. To overcome this limitation, we have developed efficient surrogate models for estimating the QoS metrics of a candidate solution during the search. Our experimental results show that the use of surrogates can produce solutions with good convergence and diversity properties at a much lower computational effort.
{"title":"Surrogate-assisted optimisation of composite applications in mobile ad hoc networks","authors":"D. Efstathiou, P. McBurney, S. Zschaler, Johann Bourcier","doi":"10.1145/2576768.2598307","DOIUrl":"https://doi.org/10.1145/2576768.2598307","url":null,"abstract":"Infrastructure-less mobile ad-hoc networks enable the development of collaborative pervasive applications. Within such dynamic networks, collaboration between devices can be realised through service-orientation by abstracting device resources as services. Recently, a framework for QoS-aware service composition has been introduced which takes into account a spectrum of orchestration patterns, and enables compositions of a better QoS than traditional centralised orchestration approaches. In this paper, we focus on the automated exploration of trade-off compositions within the search space defined by this flexible composition model. For the studied problem, the evaluation of the fitness functions guiding the search process is computationally expensive because it either involves a high-fidelity simulation or actually requires calling the composite service. To overcome this limitation, we have developed efficient surrogate models for estimating the QoS metrics of a candidate solution during the search. Our experimental results show that the use of surrogates can produce solutions with good convergence and diversity properties at a much lower computational effort.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"94 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":"129636322","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}
Randomized Search heuristics are frequently applied to NP-hard combinatorial optimization problems. The runtime analysis of randomized search heuristics has contributed tremendously to their theoretical understanding. Recently, randomized search heuristics have been examined regarding their achievable progress within a fixed time budget. We follow this approach and present a first fixed budget runtime analysis for a NP-hard combinatorial optimization problem. We consider the well-known Traveling Salesperson problem (TSP) and analyze the fitness increase that randomized search heuristics are able to achieve within a given fixed budget.
{"title":"A fixed budget analysis of randomized search heuristics for the traveling salesperson problem","authors":"Samadhi Nallaperuma, F. Neumann, Dirk Sudholt","doi":"10.1145/2576768.2598302","DOIUrl":"https://doi.org/10.1145/2576768.2598302","url":null,"abstract":"Randomized Search heuristics are frequently applied to NP-hard combinatorial optimization problems. The runtime analysis of randomized search heuristics has contributed tremendously to their theoretical understanding. Recently, randomized search heuristics have been examined regarding their achievable progress within a fixed time budget. We follow this approach and present a first fixed budget runtime analysis for a NP-hard combinatorial optimization problem. We consider the well-known Traveling Salesperson problem (TSP) and analyze the fitness increase that randomized search heuristics are able to achieve within a given fixed budget.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"171 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":"121493692","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}
Natural frequency tuning is a vital engineering problem. Every structure has natural frequencies, where vibrational loading at nearby frequencies excite the structure. This causes the structure to resonate, oscillating until energy is dissipated through friction or structural failure. Examples of fragility and distress from vibrational loading include civil structures during earthquakes or aircraft rotor blades. Tuning the structure's natural frequencies away from these vibrations increases the structure's robustness. Conversely, tuning towards the frequencies caused by vibrations can channel power into energy harvesting systems. Despite its importance, natural frequency tuning is often performed ad-hoc, by attaching external vibrational absorbers to a structure. This is usually adequate only for the lowest ("fundamental") resonant frequencies, yet remains standard practice due to the unintuitive and difficult nature of the problem. Given Evolutionary Algorithms' (EA's) ability to solve these types of problems, we propose to approach this problem with the EA CPPN-NEAT to evolve multi-material structures which resonate at multiple desired natural frequencies without external damping. The EA assigns the material type of each voxel within the discretized space of the object's existing topology, preserving the object's shape and using only its material composition to shape its frequency response.
{"title":"Automated vibrational design and natural frequency tuning of multi-material structures","authors":"N. Cheney, E. Ritz, Hod Lipson","doi":"10.1145/2576768.2598362","DOIUrl":"https://doi.org/10.1145/2576768.2598362","url":null,"abstract":"Natural frequency tuning is a vital engineering problem. Every structure has natural frequencies, where vibrational loading at nearby frequencies excite the structure. This causes the structure to resonate, oscillating until energy is dissipated through friction or structural failure. Examples of fragility and distress from vibrational loading include civil structures during earthquakes or aircraft rotor blades. Tuning the structure's natural frequencies away from these vibrations increases the structure's robustness. Conversely, tuning towards the frequencies caused by vibrations can channel power into energy harvesting systems. Despite its importance, natural frequency tuning is often performed ad-hoc, by attaching external vibrational absorbers to a structure. This is usually adequate only for the lowest (\"fundamental\") resonant frequencies, yet remains standard practice due to the unintuitive and difficult nature of the problem. Given Evolutionary Algorithms' (EA's) ability to solve these types of problems, we propose to approach this problem with the EA CPPN-NEAT to evolve multi-material structures which resonate at multiple desired natural frequencies without external damping. The EA assigns the material type of each voxel within the discretized space of the object's existing topology, preserving the object's shape and using only its material composition to shape its frequency response.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"73 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":"127394082","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 variety of real-world data and networks can be described by a heavy-tailed probability distribution of its values, vertex degrees, or other significant properties, that follows the power law. Such a scale-free data and networks can be found in both natural phenomena such as protein interaction networks and gene regulation networks and man-made structures like the Internet, language, and various social networks. An efficient analysis of large scale data and networks is often impractical and various heuristic and metaheuristc sampling techniques are deployed to select smaller subsets of the data for analysis and visualisation. A key goal of data and network sampling is to select such a subset of the original data that would accurately represent the original data with respect to selected attributes. In this work we propose a novel genetic algorithm for scale-free data and network sampling and evaluate the algorithm in a series of computational experiments.
{"title":"Genetic algorithm for sampling from scale-free data and networks","authors":"P. Krömer, J. Platoš","doi":"10.1145/2576768.2598391","DOIUrl":"https://doi.org/10.1145/2576768.2598391","url":null,"abstract":"A variety of real-world data and networks can be described by a heavy-tailed probability distribution of its values, vertex degrees, or other significant properties, that follows the power law. Such a scale-free data and networks can be found in both natural phenomena such as protein interaction networks and gene regulation networks and man-made structures like the Internet, language, and various social networks. An efficient analysis of large scale data and networks is often impractical and various heuristic and metaheuristc sampling techniques are deployed to select smaller subsets of the data for analysis and visualisation. A key goal of data and network sampling is to select such a subset of the original data that would accurately represent the original data with respect to selected attributes. In this work we propose a novel genetic algorithm for scale-free data and network sampling and evaluate the algorithm in a series of computational experiments.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"2013 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":"127439460","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}
During the early phase of project development lifecycle of large scale cyber-physical systems, a large number of requirements are needed to be assigned to different stakeholders from different organizations or different departments of the same organization for reviewing, clarifying and checking their conformance to industry standards and government or other regulations. These requirements have different characteristics such as various extents of importance to the organization, complexity, and dependencies between each other, thereby requiring different effort (workload) to review and clarify. While working with our industrial partners in the domain of cyber-physical systems, we discovered an optimization problem, where an optimal solution is required for assigning requirements to different stakeholders by maximizing their familiarities to the assigned requirements while balancing the overall workload of each stakeholder. We propose a fitness function which was investigated with four search algorithms: (1+1) Evolutionary Algorithm (EA), Genetic Algorithm, and Alternating Variable Method, whereas Random Search is used as a comparison base line. We empirically evaluated their performance for finding an optimal solution using a large-scale industrial case study and 120 artificial problems with varying complexity. Results show that (1+1) EA gives the best results together with our proposed fitness function as compared to the other three algorithms.
{"title":"Applying search algorithms for optimizing stakeholders familiarity and balancing workload in requirements assignment","authors":"T. Yue, Shaukat Ali","doi":"10.1145/2576768.2598309","DOIUrl":"https://doi.org/10.1145/2576768.2598309","url":null,"abstract":"During the early phase of project development lifecycle of large scale cyber-physical systems, a large number of requirements are needed to be assigned to different stakeholders from different organizations or different departments of the same organization for reviewing, clarifying and checking their conformance to industry standards and government or other regulations. These requirements have different characteristics such as various extents of importance to the organization, complexity, and dependencies between each other, thereby requiring different effort (workload) to review and clarify. While working with our industrial partners in the domain of cyber-physical systems, we discovered an optimization problem, where an optimal solution is required for assigning requirements to different stakeholders by maximizing their familiarities to the assigned requirements while balancing the overall workload of each stakeholder. We propose a fitness function which was investigated with four search algorithms: (1+1) Evolutionary Algorithm (EA), Genetic Algorithm, and Alternating Variable Method, whereas Random Search is used as a comparison base line. We empirically evaluated their performance for finding an optimal solution using a large-scale industrial case study and 120 artificial problems with varying complexity. Results show that (1+1) EA gives the best results together with our proposed fitness function as compared to the other three algorithms.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"392 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":"134130665","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：