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The 2003 Congress on Evolutionary Computation, 2003. CEC '03.最新文献

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A new hybrid structure genetic programming in symbolic regression 符号回归中一种新的混合结构遗传规划
The 2003 Congress on Evolutionary Computation, 2003. CEC '03. Pub Date : 2003-12-08 DOI: 10.1109/CEC.2003.1299850
Shengguang Xiong, Wei-wu Wang
{"title":"A new hybrid structure genetic programming in symbolic regression","authors":"Shengguang Xiong, Wei-wu Wang","doi":"10.1109/CEC.2003.1299850","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299850","url":null,"abstract":"Genetic programming (GP) has been applied to symbolic regression problem for a long time. The symbolic regression is to discover a function that can fit a finite set of sample data. These sample data can be guided by a simple function, which is continuous and smooth. But in a complex system, they can be produced by a discontinuous or non-smooth function. When conventional GP is applied to this complex system's modelling, it gets poor performance. This paper proposes a new GP representation and algorithm that can be applied to both continuous function's and discontinuous function's regression. Our approach is able to identify both simultaneously the function's structure and the discontinuity points. The numerical experimental results will show that the new GP is able to gain higher success rate, higher convergence rate and better solutions than conventional GP.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125525857","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: 6
An evolutionary algorithm with population immunity and its application on autonomous robot control 一种群体免疫进化算法及其在自主机器人控制中的应用
The 2003 Congress on Evolutionary Computation, 2003. CEC '03. Pub Date : 2003-12-08 DOI: 10.1109/CEC.2003.1299603
Lei Wang, B. Hirsbrunner
{"title":"An evolutionary algorithm with population immunity and its application on autonomous robot control","authors":"Lei Wang, B. Hirsbrunner","doi":"10.1109/CEC.2003.1299603","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299603","url":null,"abstract":"The natural immune system is an important resource full of inspirations for the theory researchers and the engineering developers to design some powerful information processing methods aiming at difficult problems. Based on this consideration, a novel optimal-searching algorithm, the immune mechanism based evolutionary algorithm - IMEA, is proposed for the purpose of finding an optimal/quasi-optimal solution in a multi-dimensional space. Different from the ordinary evolutionary algorithms, on one hand, due to the long-term memory, IMEA has a better capability of learning from its experience, and on the other hand, with the clonal selection, it is able to keep from the premature convergence of population. With the simulation on autonomous robot control, it is proved that IMEA is good at the task of adaptive adjustment (offline), and it can improve the robot's capability of reinforcement learning, so as to make itself able to sense its surrounding dynamic environment.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127025233","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: 10
A hybrid multiobjective evolutionary algorithm for solving truck and trailer vehicle routing problems 一种求解卡车和挂车路径问题的混合多目标进化算法
The 2003 Congress on Evolutionary Computation, 2003. CEC '03. Pub Date : 2003-12-08 DOI: 10.1109/CEC.2003.1299936
K. Tan, Tong-heng Lee, Y. H. Chew, L. Lee
{"title":"A hybrid multiobjective evolutionary algorithm for solving truck and trailer vehicle routing problems","authors":"K. Tan, Tong-heng Lee, Y. H. Chew, L. Lee","doi":"10.1109/CEC.2003.1299936","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299936","url":null,"abstract":"This paper considers a transportation problem for moving empty or laden containers for a logistic company. A model for this truck and trailer vehicle routing problem (TTVRP) is first constructed in the paper. The solution to the TTVRP consists of finding a complete routing schedule for serving the jobs with minimum routing distance and number of trucks, subject to a number of constraints such as time windows and availability and multimodal combinatorial optimization problem, a hybrid multiobjective evolutionary algorithm (HMOEA) is applied to find the Pareto optimal routing solutions for the TTVRP. Detailed analysis is performed to extract useful decision-making information from the multiobjective optimization results. The computational results have shown that the HMOEA is effective for solving multiobjective combinatorial problems, such as finding useful trade-off solutions for the TTVRP.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116569599","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: 24
Support vector clustering for multiclass classification problems 多类分类问题的支持向量聚类
The 2003 Congress on Evolutionary Computation, 2003. CEC '03. Pub Date : 2003-12-08 DOI: 10.1109/CEC.2003.1299845
Bing-Yu Sun, De-shuang Huang
{"title":"Support vector clustering for multiclass classification problems","authors":"Bing-Yu Sun, De-shuang Huang","doi":"10.1109/CEC.2003.1299845","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299845","url":null,"abstract":"We propose a new approach by combining support vector clustering (SVC) with a two layered neural network for multiclass classification problems. This new approach is not only capable of improving the performance of the traditional SVC, but also, compared to support vector machines (SVM), can reduce the computational complexity and get better performance in the case of multiclass problems. Finally, the experimental results demonstrate the effectiveness and efficiency of our approach.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122589100","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
A simplified artificial life model for multiobjective optimisation: a preliminary report 一种简化的多目标优化人工生命模型:初步报告
The 2003 Congress on Evolutionary Computation, 2003. CEC '03. Pub Date : 2003-12-08 DOI: 10.1109/CEC.2003.1299823
Adam Berry, P. Vamplew
{"title":"A simplified artificial life model for multiobjective optimisation: a preliminary report","authors":"Adam Berry, P. Vamplew","doi":"10.1109/CEC.2003.1299823","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299823","url":null,"abstract":"Recent research in the field of multiobjective optimisation (MOO) has been focused on achieving the Pareto optimal front by explicitly analysing the dominance level of individual solutions. While such approaches have produced good results for a variety of problems, they are computationally expensive due to the complexities of deriving the dominance level for each solution against the entire population. TB/spl I.bar/MOO (threshold based multiobjective optimisation) is a new artificial life approach to MOO problems that does not analyse dominance, nor perform any agent-agent comparisons. This reduction in complexity results in a significant decrease in processing overhead. Results show that TB/spl I.bar/MOO performs comparably, and often better, than its more complicated counter-parts with respect to distance from the Pareto optimal front, but is slightly weaker in terms of distribution and extent.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122758258","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: 1
Artificial immune system (AIS) research in the last five years 人工免疫系统(AIS)的研究进展
The 2003 Congress on Evolutionary Computation, 2003. CEC '03. Pub Date : 2003-12-08 DOI: 10.1109/CEC.2003.1299565
D. Dasgupta, Zhou Ji, F. González
{"title":"Artificial immune system (AIS) research in the last five years","authors":"D. Dasgupta, Zhou Ji, F. González","doi":"10.1109/CEC.2003.1299565","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299565","url":null,"abstract":"Immunity-based techniques are gaining popularity in wide area of applications, and emerging as a new branch of artificial intelligence (AI). The paper surveys the major works in this field during the last five years, in particular, it reviews the works of existing methods and the new initiatives.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117254405","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: 262
A fast algorithm on finding the non-dominated set in multi-objective optimization 多目标优化中非支配集的快速查找算法
The 2003 Congress on Evolutionary Computation, 2003. CEC '03. Pub Date : 2003-12-08 DOI: 10.1109/CEC.2003.1299411
L. Ding, Sanyou Zeng, Lishan Kang
{"title":"A fast algorithm on finding the non-dominated set in multi-objective optimization","authors":"L. Ding, Sanyou Zeng, Lishan Kang","doi":"10.1109/CEC.2003.1299411","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299411","url":null,"abstract":"A fast algorithm is proposed to find the nondominated set for multiobjective optimization problems in this paper. Two accelerated techniques are adopted in the algorithm. One is that the algorithm can yield an integer rank set after it indexes the search space. Based on this, the goal is changed into determination of the nondominated set of the integer rank set. The other is that the nondominated check sequence follows that the likely nondominated members are first checked, and that the check process is stopped when the remaining members in the nondominated check sequence are dominated. The computational complexity of the new algorithm is analyzed theoretically. Experimental results show that the new method performs much better than KLP (a famous effective algorithm) when the search space contains a large nondominated set. Moreover, the two new techniques introduced in this paper are very useful for multiobjective evolutionary algorithms (MOEAs) to improve the computational speed.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130353066","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: 44
Evolutionary search in inductive equational logic programming 归纳方程逻辑规划中的进化搜索
The 2003 Congress on Evolutionary Computation, 2003. CEC '03. Pub Date : 2003-12-08 DOI: 10.1109/CEC.2003.1299392
L. Hamel
{"title":"Evolutionary search in inductive equational logic programming","authors":"L. Hamel","doi":"10.1109/CEC.2003.1299392","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299392","url":null,"abstract":"Concept learning is the induction of a description from a set of examples. Inductive logic programming can be considered a special case of the general notion of concept learning specifically referring to the induction of first-order theories. Both concept learning and inductive logic programming can be seen as a search over all possible sentences in some representation language for sentences that correctly explain the examples and also generalize to other sentences that are part of that concept. We explore inductive logic programming with equational logic as the representation language. We present a high-level overview of the implementation of inductive equational logic using genetic programming and discuss encouraging results based on experiments that are intended to emulate real world scenarios.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130375342","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: 6
CIRG@UP OptiBench: a statistically sound framework for benchmarking optimisation algorithms CIRG@UP OptiBench:一个统计上健全的基准优化算法框架
The 2003 Congress on Evolutionary Computation, 2003. CEC '03. Pub Date : 2003-12-08 DOI: 10.1109/CEC.2003.1299386
E. Peer, A. Engelbrecht, F. V. D. Bergh
{"title":"CIRG@UP OptiBench: a statistically sound framework for benchmarking optimisation algorithms","authors":"E. Peer, A. Engelbrecht, F. V. D. Bergh","doi":"10.1109/CEC.2003.1299386","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299386","url":null,"abstract":"This article is a proposal, by the Computational Intelligence Research Group at the University of Pretoria (CIRG@UP), for a framework to benchmark optimisation algorithms. This framework, known as OptiBench, was conceived out of the necessity to consolidate the efforts of a large research group. Many problems arise when different people work independently on their own research initiatives. These problems range from duplicating effort to, more seriously, having conflicting results. In addition, less experienced members of the group are sometimes unfamiliar with the necessary statistical methods required to properly analyse their results. These problems are not limited internally to CIRG@UP but are also prevalent in the research community at large. This proposal aims to standardise the research methodology used by CIRG@UP internally (initially in the optimisation subgroup and later in subgroups working in other paradigms of computational research). Obviously this article cannot dictate the methodologies that should be used by other members of the broader research community, however, the hope is that this framework can be found useful and that others would willingly contribute and become involved.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129219157","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: 6
Adapting mutations in genetic algorithms using gene flow principles 利用基因流原理在遗传算法中适应突变
The 2003 Congress on Evolutionary Computation, 2003. CEC '03. Pub Date : 2003-12-08 DOI: 10.1109/CEC.2003.1299833
G. Greenwood
{"title":"Adapting mutations in genetic algorithms using gene flow principles","authors":"G. Greenwood","doi":"10.1109/CEC.2003.1299833","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299833","url":null,"abstract":"Bit mutation in genetic algorithms is usually done with a single fixed probability. Methods to adapt this probability have been suggested, but they operate at the genome level. This paper describes a gene level adaption scheme, based on allele frequencies, which gives a better escape from local optima.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123637508","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
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