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Opposition-Based Learning in Compact Differential Evolution
, 2011 This paper proposes the integration of the generalized opposition based learning into compact Differential Evolution frameworks and tests its impact on the algorithmic performance. Opposition-based learning is a technique which has been applied, in several circumstances, to enhance the performance of Differential Evolution.
Iacca, Giovanni +2 more
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Discourses of ‘equivalence’ in HE and notions of student engagement : resisting the neoliberal university [PDF]
, 2014 Copyright © 2014 Nadia Edmond and Jon Berry. This is an open access journal article distributed under the Creative Commons Attribution License, which permits the unrestricted use, distribution, and reproduction in any medium, provided the original work ...
Berry, Jon, Edmond, Nadia
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Learning preferences from paired opposite-based semantics
International Journal of Approximate Reasoning, 2017 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Camilo Franco +2 more
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Hybrid Algorithm of Slime Mould Algorithm and Arithmetic Optimization Algorithm Based on Random Opposition-Based Learning [PDF]
Jisuanji kexue yu tansuo, 2022 Slime mould algorithm (SMA) and arithmetic optimization algorithm (AOA) are new meta-heuristic optimization algorithms proposed recently. SMA has strong ability of global exploration, but the oscillation effect is weak in the late iteration.
JIA Heming, LIU Yuxiang, LIU Qingxin, WANG Shuang, ZHENG Rong
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Creativity and Conflict: How theory and practice shape student identities in design education [PDF]
, 2009 By exploring the role of student identities in shaping attitudes to learning, this study asks how design students draw on experience to work across theory and practice.
New, Christopher, Tynan, Jane
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Chicken Swarm Optimization Based on Elite Opposition‐Based Learning
Mathematical Problems in Engineering, 2017 Chicken swarm optimization is a new intelligent bionic algorithm, simulating the chicken swarm searching for food in nature. Basic algorithm is likely to fall into a local optimum and has a slow convergence rate. Aiming at these deficiencies, an improved chicken swarm optimization algorithm based on elite opposition‐based learning is proposed.
Chiwen Qu +3 more
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Adaptive Constrained Differential Evolution Algorithm by Using Generalized Opposition-Based Learning
Xibei Gongye Daxue Xuebao, 2019 Differential evolution is a global optimization algorithm based on greedy competition mechanism, which has the advantages of simple structure, less control parameters, higher reliability and convergence. Combining with the constraint-handling techniques,
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Learning Opposites with Evolving Rules
, 2015 The idea of opposition-based learning was introduced 10 years ago. Since then a noteworthy group of researchers has used some notions of oppositeness to improve existing optimization and learning algorithms.
Rahnamayan, Shahryar, Tizhoosh, Hamid R.
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Opposition-Based Reinforcement Learning
Journal of Advanced Computational Intelligence and Intelligent Informatics, 2006 Reinforcement learning is a machine intelligence scheme for learning in highly dynamic, probabilistic environments. By interaction with the environment, reinforcement agents learn optimal control policies, especially in the absence of a priori knowledge and/or a sufficiently large amount of training data.
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Opposition based Ensemble Micro Differential Evolution
, 2017 Differential evolution (DE) algorithm with a small population size is called Micro-DE (MDE). A small population size decreases the computational complexity but also reduces the exploration ability of DE by limiting the population diversity. In this paper,
Rahnamayan, Shahryar +2 more
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