The Experts below are selected from a list of 30030 Experts worldwide ranked by ideXlab platform
S J Ovaska - One of the best experts on this subject based on the ideXlab platform.
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a general framework for statistical performance comparison of Evolutionary Computation algorithms
Information Sciences, 2008Co-Authors: David Shilane, Jarno Martikainen, Sandrine Dudoit, S J OvaskaAbstract:This paper proposes a statistical methodology for comparing the performance of Evolutionary Computation algorithms. A twofold sampling scheme for collecting performance data is introduced, and these data are analyzed using bootstrap-based multiple hypothesis testing procedures. The proposed method is sufficiently flexible to allow the researcher to choose how performance is measured, does not rely upon distributional assumptions, and can be extended to analyze many other randomized numeric optimization routines. As a result, this approach offers a convenient, flexible, and reliable technique for comparing algorithms in a wide variety of applications.
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a general framework for statistical performance comparison of Evolutionary Computation algorithms
International conference on Artificial intelligence and applications, 2006Co-Authors: David Shilane, Jarno Martikainen, Sandrine Dudoit, S J OvaskaAbstract:This paper proposes a statistical methodology for comparing the performance of Evolutionary Computation algorithms. A two-fold sampling scheme for collecting performance data is introduced, and this data is assessed using a multiple hypothesis testing framework relying on a bootstrap resampling procedure. The proposed method offers a convenient, flexible, and reliable approach to comparing algorithms in a wide variety of applications.
Alice M. Agogino - One of the best experts on this subject based on the ideXlab platform.
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original articles interactive hybrid Evolutionary Computation for mems design synthesis
Mathematics and Computers in Simulation, 2012Co-Authors: Ying Zhang, Alice M. AgoginoAbstract:Abstract: An interactive hybrid Evolutionary Computation (IHC) process for MEMS design synthesis is described, which uses both human expertise and local performance improvement to augment the performance of an Evolutionary process. The human expertise identifies good design patterns, and local optimization fine-tunes these designs so that they reach their potential at early stages of the Evolutionary process. At the same time, the feedback on local optimal designs confirms and refines the human assessment. The advantages of the IHC process are demonstrated with micromachined resonator test cases. Guidelines on how to set parameters for the IHC algorithm are also made based on experimental observations and results.
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optimized design of mems by Evolutionary multi objective optimization with interactive Evolutionary Computation
Genetic and Evolutionary Computation Conference, 2004Co-Authors: Raffi Kamalian, Hideyuki Takagi, Alice M. AgoginoAbstract:We combine interactive Evolutionary Computation (IEC) with existing Evolutionary synthesis software for the design of micromachined resonators and evaluate its effectiveness using human evaluation of the final designs and a test for statistical significance of the improvements. The addition of IEC produces superior designs with fewer potential design or manufacturing problems than those produced through the Evolutionary synthesis software alone as it takes advantage of the human ability to perceive design flaws that cannot currently be simulated. A user study has been performed to compare the effectiveness of the IEC enhanced software with the non-interactive software. The results show that the IEC-enhanced synthesis software creates a statistically significant greater number of designs rated best by users.
Xin Yao - One of the best experts on this subject based on the ideXlab platform.
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microrobots based in vivo Evolutionary Computation in two dimensional microchannel network
IEEE Transactions on Nanotechnology, 2020Co-Authors: Shaolong Shi, Xin Yao, Junfeng Xiong, Yu Zhou, Teng Jiang, Guangzhi Zhu, Kei U Cheang, Yifan ChenAbstract:In vivo Evolutionary Computation is a novel knowledge-aided, microrobots-oriented tumor targeting framework, where externally manipulable microrobots are employed to detect the cancer in the human vascular network similar to the procedure of solving an optimization problem by swarm intelligence algorithms. The microrobots play the role of Computational agents in the optimization procedure, the vascular network is the search space, and the tumor represents the maximum or minimum to be found by agents. Previous work on this topic provided basic Computational models and search strategies, which, however, were solely verified in silico. In this letter, we use Janus microparticles as magnetic microrobots, a two-dimensional microchannel network as the human vasculature, and two representative test functions as the exemplar tumor-triggered biological gradient fields to validate in vitro the orthokinetic gravitational search algorithm proposed. The results herein demonstrate the advantages of the algorithm by presenting experimental observations on the targeting performance.
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a survey on Evolutionary Computation approaches to feature selection
IEEE Transactions on Evolutionary Computation, 2016Co-Authors: Bing Xue, Mengjie Zhang, Will N Browne, Xin YaoAbstract:Feature selection is an important task in data mining and machine learning to reduce the dimensionality of the data and increase the performance of an algorithm, such as a classification algorithm. However, feature selection is a challenging task due mainly to the large search space. A variety of methods have been applied to solve feature selection problems, where Evolutionary Computation (EC) techniques have recently gained much attention and shown some success. However, there are no comprehensive guidelines on the strengths and weaknesses of alternative approaches. This leads to a disjointed and fragmented field with ultimately lost opportunities for improving performance and successful applications. This paper presents a comprehensive survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms. In addition, current issues and challenges are also discussed to identify promising areas for future research.
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recent advances in Evolutionary Computation
Journal of Computer Science and Technology, 2006Co-Authors: Xin YaoAbstract:Evolutionary Computation has experienced a tremendous growth in the last decade in both theoretical analyses and industrial applications. Its scope has evolved beyond its original meaning of “biological evolution” toward a wide variety of nature inspired Computational algorithms and techniques, including Evolutionary, neural, ecological, social and economical Computation, etc., in a unified framework. Many research topics in Evolutionary Computation nowadays are not necessarily “Evolutionary”. This paper provides an overview of some recent advances in Evolutionary Computation that have been made in CERCIA at the University of Birmingham, UK. It covers a wide range of topics in optimization, learning and design using Evolutionary approaches and techniques, and theoretical results in the Computational time complexity of Evolutionary algorithms. Some issues related to future development of Evolutionary Computation are also discussed.
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dynamic salting route optimisation using Evolutionary Computation
Congress on Evolutionary Computation, 2005Co-Authors: Hisashi Handa, Lee Chapman, Xin YaoAbstract:On marginal winter nights, highway authorities face a difficult decision as to whether or not to salt the road network. The consequences of making a wrong decision are serious, as an untreated network is a major hazard. However, if salt is spread when it is not actually required, there are unnecessary financial and environmental consequences. In this paper, a new salting route optimisation system is proposed which combines Evolutionary Computation (EC) with the next generation road weather information systems (XRWIS). XRWIS is a new high resolution forecast system which predicts road surface temperature and condition across the road network over a 24 hour period. ECs are used to optimise a series of salting routes for winter gritting by considering XRWIS temperature data along with treatment vehicle and road network constraints. This synergy realises daily dynamic routing and it will yield considerable benefits for areas with a marginal ice problem
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Evolutionary Computation theory and applications
1999Co-Authors: Xin YaoAbstract:An introduction to Evolutionary Computation Evolutionary algorithms as search algorithms theoretical analysis of Evolutionary algorithms advanced search operators in Evolutionary algorithms parallel Evolutionary algorithms a comparison of simulated annealing and an Evolutionary algorithm on traveling salesman problems power system design and management by Evolutionary algorithms telecommunications network design and management by Evolutionary algorithms an optimization tool based on Evolutionary algorithms the evolution of artificial neural network architectures an experimental study of generalization in Evolutionary learning an Evolutionary approach to the N-person prisoner's dilemma game automated design and generalisation of heuristics high-order credit assignment in classifier systems.
Zbigniew Michalewicz - One of the best experts on this subject based on the ideXlab platform.
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Evolutionary Computation for multicomponent problems opportunities and future directions
Optimization in Industry, 2019Co-Authors: Zbigniew Michalewicz, Mohammad Reza Bonyadi, Markus Wagner, Frank NeumannAbstract:Over the past 30 years, many researchers in the field of Evolutionary Computation have put a lot of effort to introduce various approaches for solving hard problems. Most of these problems have been inspired by major industries so that solving them, by providing either optimal or near optimal solution, was of major significance. Indeed, this was a very promising trajectory as advances in these problem-solving approaches could result in adding values to major industries. In this chapter, we revisit this trajectory to find out whether the attempts that started three decades ago are still aligned with the same goal, as complexities of real-world problems increased significantly. We present some examples of modern real-world problems, discuss why they might be difficult to solve, and whether there is any mismatch between these examples and the problems that are investigated in the Evolutionary Computation area.
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adaptation in Evolutionary Computation a survey
IEEE International Conference on Evolutionary Computation, 1997Co-Authors: Robert Hinterding, Zbigniew Michalewicz, A E EibenAbstract:Adaptation of parameters and operators is one of the most important and promising areas of research in Evolutionary Computation; it tunes the algorithm to the problem while solving the problem. In this paper we develop a classification of adaptation on the basis of the mechanisms used, and the level at which adaptation operates within the Evolutionary algorithm. The classification covers all forms of adaptation in Evolutionary Computation and suggests further research.
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handbook of Evolutionary Computation
1997Co-Authors: Thomas Back, David B. Fogel, Zbigniew MichalewiczAbstract:From the Publisher: Many scientists and engineers now use the paradigms of Evolutionary Computation (genetic agorithms, evolution strategies, Evolutionary programming, genetic programming, classifier systems, and combinations or hybrids thereof) to tackle problems that are either intractable or unrealistically time consuming to solve through traditional Computational strategies. Recently there have been vigorous initiatives to promote cross-fertilization between the EC paradigms, and also to combine these paradigms with other approaches such as neural networks to create hybrid systems with enhanced capabilities. To address the need for speedy dissemination of new ideas in these fields, and also to assist in cross-disciplinary communications and understanding, Oxford University Press and the Institute of Physics have joined forces to create a major reference publication devoted to EC fundamentals, models, algorithms and applications. This work is intended to become the standard reference resource for the Evolutionary Computation community. The Handbook of Evolutionary Computation will be available in loose-leaf print form, as well as in an electronic version that combines both CD-ROM and on-line (World Wide Web) acess to its contents. Regularly published supplements will be available on a subscription basis.
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Evolutionary Computation at the edge of feasibility
Parallel Problem Solving from Nature, 1996Co-Authors: Marc Schoenauer, Zbigniew MichalewiczAbstract:Numerical optimization problems enjoy a significant popularity in Evolutionary Computation community; all major Evolutionary techniques use such problems for various tests and experiments. However, many of these techniques (as well as other, classical optimization methods) encounter difficulties in solving some real-world problems which include non-trivial constraints. This paper discusses a new development which is based on the observation that very often the global solution lies on the boundary of the feasible region. Thus, for many constrained numerical optimization problems it might be beneficial to limit the search to that boundary, using problem-specific operators. Two test cases illustrate this approach: specific operators are designed from the simple analytical expression of the constraints. Some possible generalizations to larger classes of constraints are discussed as well.
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heuristic methods for Evolutionary Computation techniques
Journal of Heuristics, 1996Co-Authors: Zbigniew MichalewiczAbstract:Evolutionary Computation techniques, which are based on a powerful principle of evolution—survival of the fittest, constitute an interesting category of heuristic search. In other words, Evolutionary techniques are stochastic algorithms whose search methods model some natural phenomena: genetic inheritance and Darwinian strife for survival. Any Evolutionary algorithm applied to a particular problem must address the issue of genetic representation of solutions to the problem and genetic operators that would alter the genetic composition of offspring during the reproduction process. However, additional heuristics should be incorporated in the algorithm as well; some of these heuristic rules provide guidelines for evaluating (feasible and infeasible) individuals in the population. This paper surveys such heuristics and discusses their merits and drawbacks.
Yohei Maruyama - One of the best experts on this subject based on the ideXlab platform.
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flight demonstration of realtime path planning of an uav using Evolutionary Computation and rule based hybrid method
International Journal of Engineering, 2018Co-Authors: Shinichiro Higashino, Yohei MaruyamaAbstract:A method for path planning of an UAV while avoiding obstacles using Evolutionary Computation and rule-based hybrid method is proposed, and its effectiveness is demonstrated successfully by flights using a small UAV. Evolutionary Computation is used for the optimization of the traveling order of the waypoints which are specified for a certain mission, and for the optimization of the number and positions of the additional waypoints inserted in order to avoid obstacles. The additional waypoints are inserted following the predetermined rule so that the efficiency of the obstacle avoidance improves.
