Results 261 to 270 of about 299,863 (342)
Some of the next articles are maybe not open access.
Multi-Objective Neural Evolutionary Algorithm for Combinatorial Optimization Problems
IEEE Transactions on Neural Networks and Learning Systems, 2021There has been a recent surge of success in optimizing deep reinforcement learning (DRL) models with neural evolutionary algorithms. This type of method is inspired by biological evolution and uses different genetic operations to evolve neural networks ...
Yinan Shao +6 more
semanticscholar +1 more source
Multi-Objective Evolutionary Algorithms
2016The primary objective in designing appropriate particle reinforced polyurethane composite which will be used as a mould material in soft tooling process is to minimize the cycle time of soft tooling process by providing faster cooling rate during solidification of wax/plastic component.
A. K. Nandi, K. Deb
openaire +2 more sources
Evolutionary Rough Parallel Multi-Objective Optimization Algorithm
Fundamenta Informaticae, 2010A hybrid unsupervised learning algorithm, which is termed as Parallel Rough-based Archived Multi-Objective Simulated Annealing (PARAMOSA), is proposed in this article. It comprises a judicious integration of the principles of the rough sets theory and the scalable distributed paradigm with the archived multi-objective simulated annealing approach ...
Maulik, Ujjwal, Sarkar, Anasua
openaire +3 more sources
Data Structures in Multi-Objective Evolutionary Algorithms
Journal of Computer Science and Technology, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Altwaijry, Najwa +1 more
openaire +2 more sources
International Journal of Production Research, 2018
With the increasing attention on environment issues, green scheduling in manufacturing industry has been a hot research topic. As a typical scheduling problem, permutation flow shop scheduling has gained deep research, but the practical case that ...
Enda Jiang, Ling Wang
semanticscholar +1 more source
With the increasing attention on environment issues, green scheduling in manufacturing industry has been a hot research topic. As a typical scheduling problem, permutation flow shop scheduling has gained deep research, but the practical case that ...
Enda Jiang, Ling Wang
semanticscholar +1 more source
Ensemble Learning Using Multi-Objective Evolutionary Algorithms
Journal of Mathematical Modelling and Algorithms, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chandra, Arjun, Yao, Xin
openaire +2 more sources
Indicator-based Multi-objective Evolutionary Algorithms
ACM Computing Surveys, 2020For over 25 years, most multi-objective evolutionary algorithms (MOEAs) have adopted selection criteria based on Pareto dominance. However, the performance of Pareto-based MOEAs quickly degrades when solving multi-objective optimization problems (MOPs) having four or more objective functions (the so-called many-objective optimization problems), mainly ...
Jesús Guillermo Falcón-Cardona +1 more
openaire +1 more source
IEEE/ACM Transactions on Computational Biology & Bioinformatics, 2018
The problem of predicting the three-dimensional (3-D) structure of a protein from its one-dimensional sequence has been called the “holy grail of molecular biology”, and it has become an important part of structural genomics projects.
Shangce Gao +4 more
semanticscholar +1 more source
The problem of predicting the three-dimensional (3-D) structure of a protein from its one-dimensional sequence has been called the “holy grail of molecular biology”, and it has become an important part of structural genomics projects.
Shangce Gao +4 more
semanticscholar +1 more source
Dynamic Multi-objective Optimization Evolutionary Algorithm
Third International Conference on Natural Computation (ICNC 2007), 2007A new evolutionary algorithm for Dynamic multiobjective optimization is proposed in this paper. First, the time period is divided into several random subperiods. In each subperiod, the problem is approximated by a static multi- objective optimization problem.
Chun-an Liu, Yuping Wang
openaire +1 more source
Multi-Objective Evolutionary Algorithms
2009Real world optimization problems are often too complex to be solved through analytical means. Evolutionary algorithms, a class of algorithms that borrow paradigms from nature, are particularly well suited to address such problems. These algorithms are stochastic methods of optimization that have become immensely popular recently, because they are ...
Sanjoy Das, Bijaya K. Panigrahi
openaire +1 more source