Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation最新文献_第3页

Playing regex golf with genetic programming 用遗传程序玩正则表达式高尔夫

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598333

Alberto Bartoli, A. D. Lorenzo, Eric Medvet, F. Tarlao

{"title":"Playing regex golf with genetic programming","authors":"Alberto Bartoli, A. D. Lorenzo, Eric Medvet, F. Tarlao","doi":"10.1145/2576768.2598333","DOIUrl":"https://doi.org/10.1145/2576768.2598333","url":null,"abstract":"Regex golf has recently emerged as a specific kind of code golf, i.e., unstructured and informal programming competitions aimed at writing the shortest code solving a particular problem. A problem in regex golf consists in writing the shortest regular expression which matches all the strings in a given list and does not match any of the strings in another given list. The regular expression is expected to follow the syntax of a specified programming language, e.g., Javascript or PHP. In this paper, we propose a regex golf player internally based on Genetic Programming. We generate a population of candidate regular expressions represented as trees and evolve such population based on a multi-objective fitness which minimizes the errors and the length of the regular expression. We assess experimentally our player on a popular regex golf challenge consisting of 16 problems and compare our results against those of a recently proposed algorithm---the only one we are aware of.Our player obtains scores which improve over the baseline and are highly competitive also with respect to human players. The time for generating a solution is usually in the order of tens minutes, which is arguably comparable to the time required by human players.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"24 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":"130913848","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}

引用次数: 22

Multiple feature construction for effective biomarker identification and classification using genetic programming 基于遗传规划的生物标志物识别分类多特征构建

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598292

Soha Ahmed, Mengjie Zhang, Lifeng Peng, Bing Xue

{"title":"Multiple feature construction for effective biomarker identification and classification using genetic programming","authors":"Soha Ahmed, Mengjie Zhang, Lifeng Peng, Bing Xue","doi":"10.1145/2576768.2598292","DOIUrl":"https://doi.org/10.1145/2576768.2598292","url":null,"abstract":"Biomarker identification, i.e., detecting the features that indicate differences between two or more classes, is an important task in omics sciences. Mass spectrometry (MS) provide a high throughput analysis of proteomic and metabolomic data. The number of features of the MS data sets far exceeds the number of samples, making biomarker identification extremely difficult. Feature construction can provide a means for solving this problem by transforming the original features to a smaller number of high-level features. This paper investigates the construction of multiple features using genetic programming (GP) for biomarker identification and classification of mass spectrometry data. In this paper, multiple features are constructed using GP by adopting an embedded approach in which Fisher criterion and p-values are used to measure the discriminating information between the classes. This produces nonlinear high-level features from the low-level features for both binary and multi-class mass spectrometry data sets. Meanwhile, seven different classifiers are used to test the effectiveness of the constructed features. The proposed GP method is tested on eight different mass spectrometry data sets. The results show that the high-level features constructed by the GP method are effective in improving the classification performance in most cases over the original set of features and the low-level selected features. In addition, the new method shows superior performance in terms of biomarker detection rate.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"23 12 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":"130223986","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}

引用次数: 47

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation 2014年遗传与进化计算年会论文集

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768

C. Igel, D. Arnold

{"title":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","authors":"C. Igel, D. Arnold","doi":"10.1145/2576768","DOIUrl":"https://doi.org/10.1145/2576768","url":null,"abstract":"On behalf of the Organizing Committee, I would like to welcome you to Vancouver for the 2014 Genetic and Evolutionary Computation Conference (GECCO 2014). This years GECCO is comprised of 20 tracks, including the new Artificial Immune Systems and Hot Off the Press (HOP) tracks. The latter offers authors of outstanding research recently published in journals and other conferences the opportunity to present their work to the GECCO community. Under the guidance of Editor-in-Chief Christian Igel, the Track Chairs and Program Committee have selected 180 out of the 544 submissions received in all tracks (excluding HOP) for oral presentation as full papers, resulting in an acceptance rate of 33%. Close to 100 short papers will be presented in the poster session. \u0000 \u0000Highlights of the conference include keynote talks by Yoshua Bengio on \"Deep Learning and Cultural Evolution\", and by Dario Floreano on \"Bridging Natural and Artificial Evolution\", as well as an invited talk by Sumit Gulwani in the Genetic Programming track. Altogether 32 tutorials cover topics ranging from broad and introductory to specialized and at the frontier of current research. GECCO also hosts fifteen workshops, including several new ones as well as at least one that predates GECCO itself. Further high points include the 11th Annual \"Humies\" Awards for Human- Competitive Results, which are again generously supported by John Koza, and five competitions, ranging from Art, Design, and Creativity to the Industrial Challenge. Finally, Evolutionary Computation in Practice continues to be an important and integral part of GECCO.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"63 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":"127871224","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}

引用次数: 4

Search for maximal snake-in-the-box using new genetic algorithm 使用新的遗传算法搜索最大的盒子里的蛇

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598296

Kim-Hang Ruiz

{"title":"Search for maximal snake-in-the-box using new genetic algorithm","authors":"Kim-Hang Ruiz","doi":"10.1145/2576768.2598296","DOIUrl":"https://doi.org/10.1145/2576768.2598296","url":null,"abstract":"The \"Snake-In-The-Box\" (SIB) problem is a challenging combinatorial search problem to find the longest constrained open path (k-spread snake) in n-dimensional hypercube (Qn). In addition to constructive techniques, many search algorithms such as Depth First Search (DFS), Genetic Algorithm (GA), hybrid Evolutionary Computation algorithm (EC), and Nested Monte-Carlo Search (NMCS) have been used to tackle this problem. To get better results and to speed up the process, these techniques often used a long snake as the starting point for the search (priming/seeding). This paper reviews the hypercube fundamentals, then presents a new search technique, Mitosis Genetic Algorithm (MGA), which was applied in search for the four different spread snakes (spread 2 to 5) in seven different dimensional hypercubes (Q6 to Q13). The MGA found three new record-breaking 3-spread snakes in Q10, Q11 and Q13, all the previously known optimal snakes from spread 2 to spread 5, and the best previous known maximal 3-S9 snake of length 63. It is remarkable that it found those within minutes to hours without priming, significantly shorter than days to weeks needed in the other techniques.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"56 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":"131351561","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}

引用次数: 0

Evolutionary agent-based simulation of the introduction of new technologies in air traffic management 基于演化主体的模拟新技术在空中交通管理中的应用

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598388

L. Yliniemi, A. Agogino, Kagan Tumer

{"title":"Evolutionary agent-based simulation of the introduction of new technologies in air traffic management","authors":"L. Yliniemi, A. Agogino, Kagan Tumer","doi":"10.1145/2576768.2598388","DOIUrl":"https://doi.org/10.1145/2576768.2598388","url":null,"abstract":"Accurate simulation of the effects of integrating new technologies into a complex system is critical to the modernization of our antiquated air traffic system, where there exist many layers of interacting procedures, controls, and automation all designed to cooperate with human operators. Additions of even simple new technologies may result in unexpected emergent behavior due to complex human/machine interactions. One approach is to create high-fidelity human models coming from the field of human factors that can simulate a rich set of behaviors. However, such models are difficult to produce, especially to show unexpected emergent behavior coming from many human operators interacting simultaneously within a complex system. Instead of engineering complex human models, we directly model the emergent behavior by evolving goal directed agents, representing human users. Using evolution we can predict how the agent representing the human user reacts given his/her goals. In this paradigm, each autonomous agent in a system pursues individual goals, and the behavior of the system emerges from the interactions, foreseen or unforeseen, between the agents/actors. We show that this method reflects the integration of new technologies in a historical case, and apply the same methodology for a possible future technology.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"37 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":"116631375","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}

引用次数: 9

A continuous developmental model for wind farm layout optimization 风电场布局优化的连续发展模型

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598383

Dennis G. Wilson, Sylvain Cussat-Blanc, K. Veeramachaneni, Una-May O’Reilly, H. Luga

引用次数: 14

Monotonic functions in EC: anything but monotone! EC中的单调函数:绝不单调!

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598338

Sylvain Colin, Benjamin Doerr, Gaspard Férey

{"title":"Monotonic functions in EC: anything but monotone!","authors":"Sylvain Colin, Benjamin Doerr, Gaspard Férey","doi":"10.1145/2576768.2598338","DOIUrl":"https://doi.org/10.1145/2576768.2598338","url":null,"abstract":"To understand how evolutionary algorithms optimize the simple class of monotonic functions, Jansen (FOGA 2007) introduced the partially-ordered evolutionary algorithm (PO-EA) model and analyzed its runtime. The PO-EA is a pessimistic model of the true optimization process, hence performance guarantees for it immediately take over to the true optimization process. Based on the observation that Jansen's model leads to a process more pessimistic than what any monotonic function would, we extend his model by parametrizing the degree of pessimism. For all degrees of pessimism, and all mutation rates c/n, we give a precise runtime analysis of this process. For all degrees of pessimism lower than that of Jansen, we observe a Θ(n log n) runtime for the standard mutation probability of 1/n. However, we also observe a strange double-jump behavior in terms of the mutation probability. For all non-zero degrees of pessimism, there is a threshold c ∈ R such that (i) for mutation rates c'/n with c' c we have an exponential runtime. To overcome the complicated interplay of mutation and selection in the PO-EA, by define artificial algorithms which provably (via a coupling argument) have the same asymptotic runtime, but allow a much easier computation of the drift towards the optimum.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"382 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":"132375151","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}

引用次数: 15

High dimensional search-based software engineering: finding tradeoffs among 15 objectives for automating software refactoring using NSGA-III 基于高维搜索的软件工程:在使用NSGA-III的自动化软件重构的15个目标中寻找折衷

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598366

Mohamed Wiem Mkaouer, M. Kessentini, Slim Bechikh, K. Deb, M. Cinnéide

{"title":"High dimensional search-based software engineering: finding tradeoffs among 15 objectives for automating software refactoring using NSGA-III","authors":"Mohamed Wiem Mkaouer, M. Kessentini, Slim Bechikh, K. Deb, M. Cinnéide","doi":"10.1145/2576768.2598366","DOIUrl":"https://doi.org/10.1145/2576768.2598366","url":null,"abstract":"There is a growing need for scalable search-based software engineering approaches that address software engineering problems where a large number of objectives are to be optimized. Software refactoring is one of these problems where a refactoring sequence is sought that optimizes several software metrics. Most of the existing refactoring work uses a large set of quality metrics to evaluate the software design after applying refactoring operations, but current search-based software engineering approaches are limited to using a maximum of five metrics. We propose for the first time a scalable search-based software engineering approach based on a newly proposed evolutionary optimization method NSGA-III where there are 15 different objectives to be optimized. In our approach, automated refactoring solutions are evaluated using a set of 15 distinct quality metrics. We evaluated this approach on seven large open source systems and found that, on average, more than 92% of code smells were corrected. Statistical analysis of our experiments over 31 runs shows that NSGA-III performed significantly better than two other many-objective techniques (IBEA and MOEA/D), a multi-objective algorithm (NSGA-II) and two mono-objective approaches, hence demonstrating that our NSGA-III approach represents the new state of the art in fully-automated refactoring.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"3 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134290527","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}

引用次数: 114

Evolvability metrics in adaptive operator selection 自适应算子选择中的演化度量

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598220

J. Soria-Alcaraz, G. Ochoa, Juan Martín Carpio Valadez, Héctor José Puga Soberanes

{"title":"Evolvability metrics in adaptive operator selection","authors":"J. Soria-Alcaraz, G. Ochoa, Juan Martín Carpio Valadez, Héctor José Puga Soberanes","doi":"10.1145/2576768.2598220","DOIUrl":"https://doi.org/10.1145/2576768.2598220","url":null,"abstract":"Evolvability metrics gauge the potential for fitness of an individual rather than fitness itself. They measure the local characteristics of the fitness landscape surrounding a solution. In adaptive operator selection the goal is to dynamically select from a given pool the operator to apply next during the search process. An important component of these adaptive schemes is credit assignment, whereby operators are rewarded according to their observed performance. This article brings the notion of evolvability to adaptive operator selection, by proposing an autonomous search algorithm that rewards operators according to their potential for fitness rather than their immediate fitness improvement. The approach is tested within an evolutionary algorithm framework featuring several mutation operators on binary strings. Three benchmark problems of increasing difficulty, Onemax, Royal Staircase and Multiple Knapsack are considered. Experiments reveal that evolvability metrics significantly improve the performance of adaptive operator selection, when compared against standard fitness improvement metrics.The main contribution is to effectively use fitness landscape metrics to guide a self-configuring algorithm.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"4 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":"133078229","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}

引用次数: 20

Applying search algorithms for optimizing stakeholders familiarity and balancing workload in requirements assignment 应用搜索算法优化涉众熟悉度，平衡需求分配中的工作量

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598309

T. Yue, Shaukat Ali

{"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}

引用次数: 16