新聞與消息 (News) 學術會議及論文徵稿 (Academic Events) 2006 IEEE Congress on Evolutionary Computation
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2006 IEEE Congress on Evolutionary Computation Sheraton Vancouver Wall Centre Special Session on Constrained Real Parameter Optimization Most optimization problems have constraints of different types (e.g., physical, time, geometric, etc.) which modify the shape of the search space. During the last few years, a wide variety of metaheuristics have been designed and applied to solve constrained optimization problems. Evolutionary algorithms and most other metaheuristics, when used for optimization, naturally operate as unconstrained search techniques. Therefore, they require an additional mechanism to incorporate constraints into their fitness function. Historically, the most common approach to incorporate constraints (both in evolutionary algorithms and in mathematical programming) is the penalty functions, which were originally proposed in the 1940s and later expanded by many researchers. Penalty functions have, in general, several limitations. Particularly, they are not a very good choice when trying to solve problem in which the optimum lies in the boundary between the feasible and the infeasible regions or when the feasible region is disjoint. Additionally, penalty functions require a careful fine-tuning to determine the most appropriate penalty factors to be used with our metaheuristics. In order to overcome the limitations of penalty functions approach, researchers have proposed a number of diverse approaches to handle constraints such as fitness approximation in constrained optimization, incorporation of knowledge such as cultural approaches in constrained optimization and so on. Additionally, the analysis of the role of the search engine has also become an interesting research topic in the last few years. For example, evolution strategies (ES), evolutionary programming (EP), differential evolution (DE) and particle swarm optimization (PSO) have been found advantageous by some researchers over other metaheuristics such as the binary genetic algorithms (GA). Despite the existence constrained optimization test suites, there is an obvious need to upgrade the current test suites by considering the types of constraints (equality, inequality, linear, nonlinear, dimensionality, active, etc.), types of objective functions (linear, quadratic, nonlinear, multimodality, separability, etc.), connectivity, relative size of feasible region and so on. In addition, it would be beneficial to evaluate and, if necessary, develop novel performance measures to deal with the diverse characteristics of the constrained optimization problems. We plan to present an extended test suite and standardized evaluation measures for researchers to test their algorithms till the CEC'2006 submission deadline in late January 2006. Along with the papers, we would also optionally like participants to submit their codes and/or executables and we shall put it up on a web-site for anyone to try out. The submitted papers will be peer-reviewed by other authors and reviewers and selected authors will be invited to present their results during CEC-06. Later, we plan to put together an edited volume with more details, so that effective algorithms will be available in one volume with comparison results based on identical criterion and on identical test problems. We hope this exercise will be helpful for other researchers interested in this field and may generate new ideas for progressing the research in this area. We hope to publish the edited volume as Springer's Lecture Notes in Computer Science after the conference. The 2006 IEEE Congress on Evolutionary Computation is part of the 2006 IEEE World Congress on Computational Intelligence. Important dates:
Special Session Organizers:
Paper Submission: [Enter] Updates: Dec 5, 2005 c to java converter is found not work properly and the provided java code is not correct. New code will be provided later. Sorry for any caused inconvenience. A test data set used for testing the correctness of the code is provided. [test data] Dec 8, 2005 "Problem Definitions and Evaluation Criteria" is updated and some errors are corrected. Please download the new one. Please notice the best known solutions provided in the file are updated and bestknown.m is updated. Dec 14,2005 A study using the MATLAB Optimization Toolbox is updated. Downloads (last updated on 11/Dec/2005): Please keep the variables within the provided search bounds. If you search out of the search bounds for g02, g14, g21 and g22, you may get -Inf or NaN. Please note that the search range for g02 and g14 is 0<xi<=10, not 0<=xi<=10 Problem Definitions and Evaluation Criteria for the CEC 2006 Special Session on Constrained Real-Parameter Optimization [pdf] Code (could be employed by C/C++/C#,Matlab and Java) [Enter]
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