Results 31 to 40 of about 5,527,579 (363)
An efficient optimizer for the 0/1 knapsack problem using group counseling [PDF]
PeerJ Computer Science, 2023 The field of optimization is concerned with determining the optimal solution to a problem. It refers to the mathematical loss or gain of a given objective function.
Yazeed Yasin Ghadi +6 more
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Evolutionary algorithms and dynamic programming [PDF]
Proceedings of the 11th Annual conference on Genetic and evolutionary computation, 2009 Recently, it has been proven that evolutionary algorithms produce good results for a wide range of combinatorial optimization problems. Some of the considered problems are tackled by evolutionary algorithms that use a representation which enables them to construct solutions in a dynamic programming fashion.
Doerr, B. +4 more
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Boosting Data-Driven Evolutionary Algorithm With Localized Data Generation
IEEE Transactions on Evolutionary Computation, 2020 By efficiently building and exploiting surrogates, data-driven evolutionary algorithms (DDEAs) can be very helpful in solving expensive and computationally intensive problems. However, they still often suffer from two difficulties.
Jian-Yu Li +4 more
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Ameliorated Ensemble Strategy-Based Evolutionary Algorithm with Dynamic Resources Allocations
International Journal of Computational Intelligence Systems, 2020 In the last two decades, evolutionary computing has become the mainstream to attract the attention of the experts in both academia and industrial applications due to the advent of the fast computer with multi-core GHz processors have had a capacity of ...
Wali Khan Mashwani +3 more
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Two-Archive Evolutionary Algorithm for Constrained Multiobjective Optimization [PDF]
IEEE Transactions on Evolutionary Computation, 2017 When solving constrained multiobjective optimization problems, an important issue is how to balance convergence, diversity, and feasibility simultaneously.
Ke Li, Renzhi Chen, G. Fu, X. Yao
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An Experimental Study on Competitive Coevolution of MLP Classifiers
Mendel, 2017 This paper investigates the effectiveness and efficiency of two competitive (predator-prey) evolutionary procedures for training multi-layer perceptron classifiers: Co-Adaptive Neural Network Training, and a modified version of Co-Evolutionary Neural ...
Marco Castellani, Rahul Lalchandani
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A Customized Differential Evolutionary Algorithm for Bounded Constrained Optimization Problems
Complexity, 2021 Bound-constrained optimization has wide applications in science and engineering. In the last two decades, various evolutionary algorithms (EAs) were developed under the umbrella of evolutionary computation for solving various bound-constrained benchmark ...
Wali Khan Mashwani +4 more
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Autonomous Evolutionary Algorithm [PDF]
, 2010 Evolutionary algorithms (EA) are randomized heuristic search methods based on the principles of natural evolution (Banzhaf et al., 1998; Goldberg, 1989; Holland, 1975; Back, 1996; Koza, 1992). If we know how to describe the problem using the terminology of artificial evolution, the EAs are quite easy to apply.
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Personalized-Template-Guided Intelligent Evolutionary Algorithm
Applied SciencesExisting heuristic algorithms are based on inspiration sources and have not yet done a good job of basing themselves on optimization principles to minimize and utilize historical information, which may lead to low efficiency, accuracy, and stability of ...
Dongni Hu +4 more
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A New Hypervolume-Based Evolutionary Algorithm for Many-Objective Optimization
IEEE Transactions on Evolutionary Computation, 2020 In this article, a new hypervolume-based evolutionary multiobjective optimization algorithm (EMOA), namely, R2HCA-EMOA (R2-based hypervolume contribution approximation EMOA), is proposed for many-objective optimization.
Ke Shang, H. Ishibuchi
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