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Titlebook: Agent-Based Evolutionary Search; Ruhul Amin Sarker,Tapabrata Ray Book 2010 Springer-Verlag Berlin Heidelberg 2010 agents.algorithm.algorit

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21#
發(fā)表于 2025-3-25 05:17:51 | 只看該作者
An Agent-Based Parallel Ant Algorithm with an Adaptive Migration Controller,ent. The proposed APAA is especially suitable for large-scale problems. Experimental studies on a set of benchmark functions show that APAA can obtain better results at a faster speed for functions in high dimensional space.
22#
發(fā)表于 2025-3-25 09:33:54 | 只看該作者
23#
發(fā)表于 2025-3-25 11:50:10 | 只看該作者
1867-4534 sed Evolutionary Search.Written by leading experts in this fAgent based evolutionary search is an emerging paradigm in computational int- ligence offering the potential to conceptualize and solve a variety of complex problems such as currency trading, production planning, disaster response m- agemen
24#
發(fā)表于 2025-3-25 18:46:42 | 只看該作者
Provable Security of?, Structure based system which leads to the foundation of the agent based evolutionary algorithm. The strengths and weaknesses of these algorithms are analyzed. In addition, the contributions in this book are also discussed.
25#
發(fā)表于 2025-3-25 20:40:51 | 只看該作者
Linear-Time Oblivious Permutations for?SPDZ understanding their complex behavior as well as their limitations. The contribution is concluded with selected experimental results obtained from the application of EMAS and iEMAS to the problem of global optimization for the popular benchmark functions and for computation-costly machine learning problems.
26#
發(fā)表于 2025-3-26 00:50:27 | 只看該作者
Lecture Notes in Computer Sciencef time dependent data sets, as they are produced by evolutionary optimization algorithms. We demonstrate various multi-dimensional visualization techniques, as built into VISPLORE, which help to understand the dynamics of stochastic search algorithms.
27#
發(fā)表于 2025-3-26 07:48:05 | 只看該作者
28#
發(fā)表于 2025-3-26 08:28:25 | 只看該作者
An Attempt to Stochastic Modeling of Memetic Systems, understanding their complex behavior as well as their limitations. The contribution is concluded with selected experimental results obtained from the application of EMAS and iEMAS to the problem of global optimization for the popular benchmark functions and for computation-costly machine learning problems.
29#
發(fā)表于 2025-3-26 14:08:36 | 只看該作者
30#
發(fā)表于 2025-3-26 17:09:12 | 只看該作者
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