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Register a new userMulti-Objective Optimization Algorithms to Solve Molecular Docking
تقييم خوارزميات الأمثلة متعددة الأهداف في حل مسألة ارتباط الجزيئات
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تعتبر مسألة إيجاد الحل الأمثل لمسألة الارتباط الجزيئي بين المركبات من المسائل الصعبة. عند حل المسألة باستخدام الخوارزميات التي تتبع النهج الوحيد الهدف تكون النتائج متغيرة و معقدة. لا يمكن العثور إلاّ على عدد قليل من الأوراق البحثية التي تتناول هذه المسألة عن طريق اتباع النهج متعدد الأهداف، كما لم يتم بذل الجهد الكافي لإجراء مقارنات تجريبية في سبيل توضيح أفضل أداء لأفضل خوارزمية. يكمن هدف هذا البحث في استخدام مجموعة من خوارزميات الأمثلة متعددة الأهداف و المقارنة بينها لحل مسألة ارتباط الجزيئات بين المركبات.
Molecular docking is a hard optimization problem that has been tackled in the past, demonstrating new and challenging results when looking for one objective . However, only a few papers can be found in the literature that deal with this problem by means of a multi-objective approach, and no experimental comparisons have been made in order to clarify which of them has the best overall performance. In this research, we use and compare, a set of representative multi-objective optimization algorithms. The approach followed is focused on optimizing the inter-molecular and intra-molecular energies as two main objectives to minimize.
References used
Goodsell- D.S., Morris- G.M, 1998- Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function- pp.1639–166
Roy- R, Oduguwa- A., Tiwari- A, 2006- Multi-objective optimisation of the protein-ligand docking problem in drug discovery- Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation- Seattle- USA- pp. 1793–1800
Grosdidier- A., Zoete- V.- Michielin- O, 2007- EADock: Docking of small molecules into protein active sites with a multi-objective evolutionary optimization- pp. 1010–1025
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Multi-objective evolutionary algorithms are used in a wide range
of fields to solve the issues of optimization, which require several
conflicting objectives to be considered together. Basic evolutionary
algorithm algorithms have several drawbacks,
such as lack of a
good criterion for termination, and lack of evidence of good
convergence. A multi-objective hybrid evolutionary algorithm is
often used to overcome these defects.
optimization
الأمثلة
الأمثلة متعددة الأهداف
الخوارزميات التطورية
الخوارزميات التطورية المتعددة الأهداف
الخوارزميات التطورية عديدة الأهداف
(Multi-Objective Optimization (MO
Evolutionary Algorithms
(Multi-Objective Evolutionary Algorithms (MOEAs
(Many-Objective Evolutionary Algorithms (MaOEAs
المزيد..
In this research, we are studying the possibility of contribution
in solving the multi-objective vehicle Routing problem with time
windows , that is one of the optimization problems of the NP-hard
type , This problem has attracted a lot of attenti
on now because of
its real life applications.
Moreover, We will also introduced an algorithm called hybrid
algorithm (HA) which depends on integrates between Multiple
objective ant colony optimisation (MOACO) and tabu search (TS)
algorithm based on the Pareto optimization , and compare the
presented approach is the developer with standard tests to
demonstrate the applicability and efficiency.
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simultaneous optimization of more than one objective functions is
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the ant colony optimization algorithm with the Genetic algorithm.
Modelling and understanding dialogues in a conversation depends on identifying the user intent from the given text. Unknown or new intent detection is a critical task, as in a realistic scenario a user intent may frequently change over time and diver
t even to an intent previously not encountered. This task of separating the unknown intent samples from known intents one is challenging as the unknown user intent can range from intents similar to the predefined intents to something completely different. Prior research on intent discovery often consider it as a classification task where an unknown intent can belong to a predefined set of known intent classes. In this paper we tackle the problem of detecting a completely unknown intent without any prior hints about the kind of classes belonging to unknown intents. We propose an effective post-processing method using multi-objective optimization to tune an existing neural network based intent classifier and make it capable of detecting unknown intents. We perform experiments using existing state-of-the-art intent classifiers and use our method on top of them for unknown intent detection. Our experiments across different domains and real-world datasets show that our method yields significant improvements compared with the state-of-the-art methods for unknown intent detection.
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