Tournament versus Fitness Uniform Selection
In evolutionary algorithms a critical parameter that must be tuned is that of selection pressure. If it is set too low then the rate of convergence towards the optimum is likely to be slow. Alternatively if the selection pressure is set too high the system is likely to become stuck in a local optimu...
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sg-smu-ink.sis_research-32172018-07-13T03:42:59Z Tournament versus Fitness Uniform Selection LEGG, Shane HUTTER, Marcus KUMAR, Akshat In evolutionary algorithms a critical parameter that must be tuned is that of selection pressure. If it is set too low then the rate of convergence towards the optimum is likely to be slow. Alternatively if the selection pressure is set too high the system is likely to become stuck in a local optimum due to a loss of diversity in the population. The recent Fitness Uniform Selection Scheme (FUSS) is a conceptually simple but somewhat radical approach to addressing this problem - rather than biasing the selection towards higher fitness, FUSS biases selection towards sparsely populated fitness levels. In this paper we compare the relative performance of FUSS with the well known tournament selection scheme on a range of problems. 2004-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2217 info:doi/10.1109/CEC.2004.1331162 https://ink.library.smu.edu.sg/context/sis_research/article/3217/viewcontent/Tournament_versus_Fitness_Uniform_Selection.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Selection schemes fitness evaluation optimization fitness landscapes basic working principles of evolutionary computations (self)adaptation evolutionary algorithm deceptive & multimodal optimization problems. Artificial Intelligence and Robotics Numerical Analysis and Scientific Computing |
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Selection schemes fitness evaluation optimization fitness landscapes basic working principles of evolutionary computations (self)adaptation evolutionary algorithm deceptive & multimodal optimization problems. Artificial Intelligence and Robotics Numerical Analysis and Scientific Computing LEGG, Shane HUTTER, Marcus KUMAR, Akshat Tournament versus Fitness Uniform Selection |
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In evolutionary algorithms a critical parameter that must be tuned is that of selection pressure. If it is set too low then the rate of convergence towards the optimum is likely to be slow. Alternatively if the selection pressure is set too high the system is likely to become stuck in a local optimum due to a loss of diversity in the population. The recent Fitness Uniform Selection Scheme (FUSS) is a conceptually simple but somewhat radical approach to addressing this problem - rather than biasing the selection towards higher fitness, FUSS biases selection towards sparsely populated fitness levels. In this paper we compare the relative performance of FUSS with the well known tournament selection scheme on a range of problems. |
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LEGG, Shane HUTTER, Marcus KUMAR, Akshat |
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LEGG, Shane HUTTER, Marcus KUMAR, Akshat |
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LEGG, Shane |
title |
Tournament versus Fitness Uniform Selection |
title_short |
Tournament versus Fitness Uniform Selection |
title_full |
Tournament versus Fitness Uniform Selection |
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Tournament versus Fitness Uniform Selection |
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Tournament versus Fitness Uniform Selection |
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tournament versus fitness uniform selection |
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Institutional Knowledge at Singapore Management University |
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2004 |
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https://ink.library.smu.edu.sg/sis_research/2217 https://ink.library.smu.edu.sg/context/sis_research/article/3217/viewcontent/Tournament_versus_Fitness_Uniform_Selection.pdf |
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