Hybrid Multi-population Evolution based on Genetic Algorithm and Regularized Evolution for Neural Architecture Search

2020 ◽  
Author(s):  
Phanomphon Yotchon ◽  
Yutana Jewajinda
Keyword(s):  
Genetic Algorithm ◽  
Neural Architecture ◽  
Population Evolution

An Adaptive Genetic Algorithm Based on Multi-Population Parallel Evolutionary for Highway Alignment Optimization Model

2011 ◽  
Vol 58-60 ◽  
pp. 1499-1503 ◽  
Author(s):  
Jian Xin Chen ◽  
Yong Yi Guo ◽  
Mai Xia Lv
Keyword(s):  
Genetic Algorithm ◽  
Convergence Rate ◽  
Single Species ◽  
Adaptive Genetic Algorithm ◽  
Parallel Genetic Algorithm ◽  
Single Population ◽  
Highway Design ◽  
Population Evolution ◽  
Highway Alignment Optimization ◽  
Highway Alignment

Based on the characteristics of the highway design, this paper transfers all the factors involved in the highway design to a cost-optimized-oriented model and designs a variety parallel genetic algorithm to optimize highway design. While maintaining evolution stability of excellent individual, the algorithm can improve convergence rate and accuracy and avoid premature convergence generated by single-population evolution. To some extent, it makes up generalization-lacking defects of a single species or steady parameters in premature overcoming. Finally, the algorithm is verified with a good result. This algorithm provides a useful method for highway design.

scholarly journals An Improved Adaptive Genetic Algorithm for Two-Dimensional Rectangular Packing Problem

2021 ◽  
Vol 11 (1) ◽  
pp. 413
Author(s):  
Yi-Bo Li ◽  
Hong-Bao Sang ◽  
Xiang Xiong ◽  
Yu-Rou Li
Keyword(s):  
Genetic Algorithm ◽  
Packing Problem ◽  
Global Perspective ◽  
Adaptive Genetic Algorithm ◽  
Two Dimensional ◽  
Filling Rate ◽  
Zero Waste ◽  
Population Evolution ◽  
Rectangular Packing ◽  
The Impact

This paper proposes the hybrid adaptive genetic algorithm (HAGA) as an improved method for solving the NP-hard two-dimensional rectangular packing problem to maximize the filling rate of a rectangular sheet. The packing sequence and rotation state are encoded in a two-stage approach, and the initial population is constructed from random generation by a combination of sorting rules. After using the sort-based method as an improved selection operator for the hybrid adaptive genetic algorithm, the crossover probability and mutation probability are adjusted adaptively according to the joint action of individual fitness from the local perspective and the global perspective of population evolution. The approach not only can obtain differential performance for individuals but also deals with the impact of dynamic changes on population evolution to quickly find a further improved solution. The heuristic placement algorithm decodes the rectangular packing sequence and addresses the two-dimensional rectangular packing problem through continuous iterative optimization. The computational results of a wide range of benchmark instances from zero-waste to non-zero-waste problems show that the HAGA outperforms those of two adaptive genetic algorithms from the related literature. Compared with some recent algorithms, this algorithm, which can be increased by up to 1.6604% for the average filling rate, has great significance for improving the quality of work in fields such as packing and cutting.

HELGA: a heterogeneous encoding lifelike genetic algorithm for population evolution modeling and simulation

2014 ◽  
Vol 18 (12) ◽  
pp. 2565-2576 ◽  
Author(s):  
Monica Patrascu ◽  
Alexandra Florentina Stancu ◽  
Florin Pop
Keyword(s):  
Genetic Algorithm ◽  
Modeling And Simulation ◽  
Population Evolution ◽  
Evolution Modeling

scholarly journals QoS-Aware Mobile Service Selection Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Chengwen Zhang ◽  
Lei Zhang ◽  
Guanhua Zhang
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Service Selection ◽  
Mobile Service ◽  
Context Aware ◽  
Selection Algorithm ◽  
Sequence Composition ◽  
Population Evolution ◽  
Encoding Method ◽  
Selection Of

For the problem of mobile service selection, this paper gives a context-aware service selection algorithm based on Genetic Algorithm. In this algorithm, a tree encoding method, a fitness function, and a fitness-better strategy were proposed. The tree encoding mode made Genetic Algorithm support selection of various types of service combinations, for example, sequence composition, concurrence composition, probability composition, and loop composition. According to the encoding method, a fitness function was designed specially. The fitness-better strategy gives the direction of population evolution and avoids the degradation of population fitness. Some experiments analyses show that the provided service selection algorithm can get better service composition.

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