Comparing the performance of different evolutive Multi-Objective algorithms is an open problem. With time, many performance measures have been proposed. Unfortunately, the evaluations of many of these performance measures disagree with the common sense of when a non-dominated set is better than another. In this work we present a benchmark that is helpful to check if a performance measure actually has a good behavior. Some of the most popular performance measures in literature are tested. The results are valuable for a better understanding of what performance measures are better.
{"title":"A benchmark for quality indicators in multi-objective optimization.","authors":"G. Lizárraga, A. H. Aguirre, S. Rionda","doi":"10.1145/1569901.1570188","DOIUrl":"https://doi.org/10.1145/1569901.1570188","url":null,"abstract":"Comparing the performance of different evolutive Multi-Objective algorithms is an open problem. With time, many performance measures have been proposed. Unfortunately, the evaluations of many of these performance measures disagree with the common sense of when a non-dominated set is better than another. In this work we present a benchmark that is helpful to check if a performance measure actually has a good behavior. Some of the most popular performance measures in literature are tested. The results are valuable for a better understanding of what performance measures are better.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129425661","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 concept of artificial evolution has been applied to numerous real world applications in different domains. In this paper, we use this concept in the domain of virology to evolve computer viruses. We call this domain as "Evolvable Malware". To this end, we propose an evolutionary framework that consists of three modules: (1) a code analyzer that generates a high-level genotype representation of a virus from its machine code, (2) a genetic algorithm that uses the standard selection, cross-over and mutation operators to evolve viruses, and (3) the code generator converts the genotype of a newly evolved virus to its machinelevel code. In this paper, we validate the notion of evolution in viruses on a well-known virus family, called Bagle. The results of our proof-of-concept study show that we have successfully evolved new viruses-previously unknown and known-variants of Bagle-starting from a random population of individuals. To the best of our knowledge, this is the first empirical work on evolution of computer viruses. In future, we want to improve this proof-of-concept framework into a full-blown virus evolution engine.
{"title":"Evolvable malware","authors":"S. Noreen, Shafaq Murtaza, M. Shafiq, M. Farooq","doi":"10.1145/1569901.1570111","DOIUrl":"https://doi.org/10.1145/1569901.1570111","url":null,"abstract":"The concept of artificial evolution has been applied to numerous real world applications in different domains. In this paper, we use this concept in the domain of virology to evolve computer viruses. We call this domain as \"Evolvable Malware\". To this end, we propose an evolutionary framework that consists of three modules: (1) a code analyzer that generates a high-level genotype representation of a virus from its machine code, (2) a genetic algorithm that uses the standard selection, cross-over and mutation operators to evolve viruses, and (3) the code generator converts the genotype of a newly evolved virus to its machinelevel code. In this paper, we validate the notion of evolution in viruses on a well-known virus family, called Bagle. The results of our proof-of-concept study show that we have successfully evolved new viruses-previously unknown and known-variants of Bagle-starting from a random population of individuals. To the best of our knowledge, this is the first empirical work on evolution of computer viruses. In future, we want to improve this proof-of-concept framework into a full-blown virus evolution engine.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129814209","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}
James Smaldon, N. Krasnogor, C. Alexander, M. Gheorghe
VLSI research, in its continuous push toward further miniaturisation, is seeking to break through the limitations of current circuit manufacture techniques by moving towards biomimetic methodologies that rely on self-assembly, selforganisation and evodevo-like processes. On the other hand, Systems and Synthetic biology's quest to achieve ever more detailed (multi)cell models are relying more and more on concepts derived from computer science and engineering such as the use of logic gates, clocks and pulse generator analogs to describe a cell's decision making behavior. This paper is situated at the crossroad of these two enterprises. That is, a novel method of non-conventional computation based on the encapsulation of simple gene regulatory-like networks within liposomes is described. Three transcription Boolean logic gates were encapsulated and simulated within liposomes self-assembled from DMPC (dimyristoylphosphatidylcholine) amphiphiles using an implementation of Dissipative Particle Dynamics (DPD) created with the NVIDIA CUDA framework, and modified to include a simple collision chemistry in a stochastic environment. The response times of the AND, OR and NOT gates were shown to be positively effected by the encapsulation within the liposome inner volume.
{"title":"Liposome logic","authors":"James Smaldon, N. Krasnogor, C. Alexander, M. Gheorghe","doi":"10.1145/1569901.1569924","DOIUrl":"https://doi.org/10.1145/1569901.1569924","url":null,"abstract":"VLSI research, in its continuous push toward further miniaturisation, is seeking to break through the limitations of current circuit manufacture techniques by moving towards biomimetic methodologies that rely on self-assembly, selforganisation and evodevo-like processes. On the other hand, Systems and Synthetic biology's quest to achieve ever more detailed (multi)cell models are relying more and more on concepts derived from computer science and engineering such as the use of logic gates, clocks and pulse generator analogs to describe a cell's decision making behavior. This paper is situated at the crossroad of these two enterprises. That is, a novel method of non-conventional computation based on the encapsulation of simple gene regulatory-like networks within liposomes is described. Three transcription Boolean logic gates were encapsulated and simulated within liposomes self-assembled from DMPC (dimyristoylphosphatidylcholine) amphiphiles using an implementation of Dissipative Particle Dynamics (DPD) created with the NVIDIA CUDA framework, and modified to include a simple collision chemistry in a stochastic environment. The response times of the AND, OR and NOT gates were shown to be positively effected by the encapsulation within the liposome inner volume.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130109641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Luis Germán Pérez-Hernández, K. Rodríguez-Vázquez, R. Garduño-Juárez
This article presents the implementation of a bio-inspired algorithm, which is the algorithm of particle swarm optimization (PSO) in with the objective of minimizing the function of conformational energy ECEPP/3 for the protein folding problem (PFP) for real conformations considering structural restrictions. In this case, using a representation of torsion angles of the skeleton and the side chains, applying the sequence of amino acid of the peptide leu-enkephalin for the prediction of 3D structure of minimum energy. The quality of the results is compared with other techniques reported in literature. Subsequently, the PSO is used to predict the structure of unknown proteins.
{"title":"Parallel particle swarm optimization applied to the protein folding problem","authors":"Luis Germán Pérez-Hernández, K. Rodríguez-Vázquez, R. Garduño-Juárez","doi":"10.1145/1569901.1570163","DOIUrl":"https://doi.org/10.1145/1569901.1570163","url":null,"abstract":"This article presents the implementation of a bio-inspired algorithm, which is the algorithm of particle swarm optimization (PSO) in with the objective of minimizing the function of conformational energy ECEPP/3 for the protein folding problem (PFP) for real conformations considering structural restrictions. In this case, using a representation of torsion angles of the skeleton and the side chains, applying the sequence of amino acid of the peptide leu-enkephalin for the prediction of 3D structure of minimum energy. The quality of the results is compared with other techniques reported in literature. Subsequently, the PSO is used to predict the structure of unknown proteins.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132403661","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}
Núria Macià, A. Orriols-Puig, Ester Bernadó-Mansilla
Typical domains used in machine learning analyses only cover the complexity space partially, remaining a large proportion of problem difficulties that are not tested. Since the acquisition of new real-world problems is costly, the machine learning community has started giving importance to the automatic generation of learning domains with bounded difficulty. This paper proposes the use of an evolutionary multi-objective technique to generate artificial data sets that meet specific characteristics and fill these holes. The results show that the multi-objective evolutionary algorithm is able to create data sets of different complexities, covering most of the solution space where we had no real-world problem representatives. The proposed method is the starting point to study data complexity estimates and steps forward in the gap between data and learners.
{"title":"EMO shines a light on the holes of complexity space","authors":"Núria Macià, A. Orriols-Puig, Ester Bernadó-Mansilla","doi":"10.1145/1569901.1570229","DOIUrl":"https://doi.org/10.1145/1569901.1570229","url":null,"abstract":"Typical domains used in machine learning analyses only cover the complexity space partially, remaining a large proportion of problem difficulties that are not tested. Since the acquisition of new real-world problems is costly, the machine learning community has started giving importance to the automatic generation of learning domains with bounded difficulty. This paper proposes the use of an evolutionary multi-objective technique to generate artificial data sets that meet specific characteristics and fill these holes. The results show that the multi-objective evolutionary algorithm is able to create data sets of different complexities, covering most of the solution space where we had no real-world problem representatives. The proposed method is the starting point to study data complexity estimates and steps forward in the gap between data and learners.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130469391","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}
We describe an approach to generating animations of drawings that start as a random collection of strokes and gradually resolve into a recognizable subject. The strokes are represented as tree based genetic programs. An animation is generated by rendering the best individual in a generation as a frame of a movie. The resulting animations have an engaging characteristic in which the target slowly emerges from a random set of strokes. We have generated two qualitatively different kinds of animations, ones that use grey level straight line strokes and ones that use binary Bezier curve stokes. Around 100,000 generations are needed to generate engaging animations. Population sizes of 2 and 4 give the best convergence behaviour. Convergence can be accelerated by using information from the target in drawing a stroke. Our approach provides a large range of creative opportunities for artists. Artists have control over choice of target and the various stroke parameters.
{"title":"Animated drawings rendered by genetic programming","authors":"P. Barile, V. Ciesielski, M. Berry, K. Trist","doi":"10.1145/1569901.1570030","DOIUrl":"https://doi.org/10.1145/1569901.1570030","url":null,"abstract":"We describe an approach to generating animations of drawings that start as a random collection of strokes and gradually resolve into a recognizable subject. The strokes are represented as tree based genetic programs. An animation is generated by rendering the best individual in a generation as a frame of a movie. The resulting animations have an engaging characteristic in which the target slowly emerges from a random set of strokes. We have generated two qualitatively different kinds of animations, ones that use grey level straight line strokes and ones that use binary Bezier curve stokes. Around 100,000 generations are needed to generate engaging animations. Population sizes of 2 and 4 give the best convergence behaviour. Convergence can be accelerated by using information from the target in drawing a stroke. Our approach provides a large range of creative opportunities for artists. Artists have control over choice of target and the various stroke parameters.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128023335","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}
Currently, various automatic programming techniques have been proposed and applied in various fields. Graph Structured Program Evolution (GRAPE) is a recent automatic programming technique with graph structure. This technique can generate complex programs automatically. In this paper, we introduce the concept of automatically defined functions, called automatically defined nodes (ADN), in GRAPE. The proposed GRAPE program has a main program and several subprograms. We verified the effectiveness of ADN through several program evolution experiments, and report the results of evolution of recursive programs using GRAPE modified with ADN.
目前，各种自动编程技术已经被提出并应用于各个领域。图结构程序进化(Graph Structured Program Evolution, GRAPE)是一种基于图结构的自动编程技术。这种技术可以自动生成复杂的程序。在本文中，我们引入了自动定义函数的概念，称为自动定义节点(ADN)。提出的葡萄项目有一个主项目和几个子项目。我们通过几个程序进化实验验证了ADN的有效性，并报告了使用经过ADN修饰的GRAPE进行递归程序进化的结果。
{"title":"Graph structured program evolution with automatically defined nodes","authors":"S. Shirakawa, T. Nagao","doi":"10.1145/1569901.1570050","DOIUrl":"https://doi.org/10.1145/1569901.1570050","url":null,"abstract":"Currently, various automatic programming techniques have been proposed and applied in various fields. Graph Structured Program Evolution (GRAPE) is a recent automatic programming technique with graph structure. This technique can generate complex programs automatically. In this paper, we introduce the concept of automatically defined functions, called automatically defined nodes (ADN), in GRAPE. The proposed GRAPE program has a main program and several subprograms. We verified the effectiveness of ADN through several program evolution experiments, and report the results of evolution of recursive programs using GRAPE modified with ADN.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"69 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125389562","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 absence of a priori knowledge about global optima, initial populations in genetic algorithms (GAs) should at least be diversified, especially while dealing with large spaces. On the other hand, the use of parallel models for GAs helps to solve large instances. We will focus on the island model. In this paper we propose an island initialization technique for permutation-based problems. We exploit a virtual tree organisation commonly used in exact methods (Branch and Bound) to generate a fully disjoint and well distributed (over the search space) initial population in each island. This method can be used for all permutation-based problems (QAP, Flow-shop, Q3AP..). regardless of the number of permutations. Experiments are performed over Q3AP benchmarks using a $10$ island model. The results shows the efficiency of the proposed method especially for large instances.
{"title":"Interval island model initialization for permutation-based problems","authors":"M. Mehdi, N. Melab, E. Talbi, P. Bouvry","doi":"10.1145/1569901.1570236","DOIUrl":"https://doi.org/10.1145/1569901.1570236","url":null,"abstract":"In the absence of a priori knowledge about global optima, initial populations in genetic algorithms (GAs) should at least be diversified, especially while dealing with large spaces. On the other hand, the use of parallel models for GAs helps to solve large instances. We will focus on the island model. In this paper we propose an island initialization technique for permutation-based problems. We exploit a virtual tree organisation commonly used in exact methods (Branch and Bound) to generate a fully disjoint and well distributed (over the search space) initial population in each island. This method can be used for all permutation-based problems (QAP, Flow-shop, Q3AP..). regardless of the number of permutations. Experiments are performed over Q3AP benchmarks using a $10$ island model. The results shows the efficiency of the proposed method especially for large instances.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125584174","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}
Huynh Thi Thanh Binh, R. McKay, N. X. Hoai, N. D. Nghia
In this paper, we propose a new heuristic, called Center-Based Recursive Clustering - CBRC, for solving the bounded diameter minimum spanning tree (BDMST) problem. Our proposed hybrid genetic algorithm [12] is also extended to include the new heuristic and a multi-parent crossover operator. We test the new heuristic and genetic algorithm on two sets of benchmark problem instances for the Euclidean and Non-Euclidean cases. Experimental results show the effectiveness of the proposed heuristic and genetic algorithm.
{"title":"New heuristic and hybrid genetic algorithm for solving the bounded diameter minimum spanning tree problem","authors":"Huynh Thi Thanh Binh, R. McKay, N. X. Hoai, N. D. Nghia","doi":"10.1145/1569901.1569953","DOIUrl":"https://doi.org/10.1145/1569901.1569953","url":null,"abstract":"In this paper, we propose a new heuristic, called Center-Based Recursive Clustering - CBRC, for solving the bounded diameter minimum spanning tree (BDMST) problem. Our proposed hybrid genetic algorithm [12] is also extended to include the new heuristic and a multi-parent crossover operator. We test the new heuristic and genetic algorithm on two sets of benchmark problem instances for the Euclidean and Non-Euclidean cases. Experimental results show the effectiveness of the proposed heuristic and genetic algorithm.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123357415","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 propose a hybrid Differential Evolution (DE) algorithm based on the fuzzy C-means clustering algorithm, referred to as FCDE. The fuzzy C-means clustering algorithm is incorporated with DE to utilize the information of the population efficiently, and hence it can generate good solutions and enhance the performance of the original DE. In addition, the population-based algorithmgenerator is adopted to efficiently update the population with the clustering offspring. In order to test the performance of our approach, 13 high-dimensional benchmark functions of diverse complexities are employed. The results show that our approach is effective and efficient. Compared with other state-of-the-art DE approaches, our approach performs better, or at least comparably, in terms of the quality of the final solutions and the reduction of the number of fitness function evaluations (NFFEs).
{"title":"Hybrid differential evolution based on fuzzy C-means clustering","authors":"Wenyin Gong, Z. Cai, C. Ling, Jun Du","doi":"10.1145/1569901.1569974","DOIUrl":"https://doi.org/10.1145/1569901.1569974","url":null,"abstract":"In this paper, we propose a hybrid Differential Evolution (DE) algorithm based on the fuzzy C-means clustering algorithm, referred to as FCDE. The fuzzy C-means clustering algorithm is incorporated with DE to utilize the information of the population efficiently, and hence it can generate good solutions and enhance the performance of the original DE. In addition, the population-based algorithmgenerator is adopted to efficiently update the population with the clustering offspring. In order to test the performance of our approach, 13 high-dimensional benchmark functions of diverse complexities are employed. The results show that our approach is effective and efficient. Compared with other state-of-the-art DE approaches, our approach performs better, or at least comparably, in terms of the quality of the final solutions and the reduction of the number of fitness function evaluations (NFFEs).","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126238154","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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