Journal ArticleDOI
Genetic Algorithm and its Applications to Mechanical Engineering: A Review
Trupti Bhoskar,Omkar Kulkarni,Ninad Kulkarni,Sujata Patekar,Ganesh Kakandikar,Vilas M. Nandedkar +5 more
TLDR
Genetic algorithm is a multi-path algorithm that searches many peaks in parallel, hence reducing the possibility of local minimum trapping and solve the multi-objective optimization problems.About:
This article is published in Materials Today: Proceedings.The article was published on 2015-01-01. It has received 82 citations till now. The article focuses on the topics: Meta-optimization & Genetic representation.read more
Citations
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Journal ArticleDOI
Genetic-algorithm based framework for lattice support structure optimization in additive manufacturing
TL;DR: A new framework to optimize the geometry and topology of inner and outer support structures of Additive Manufacturing technologies, validated on several test cases of various geometries, containing both inner andouter areas to be supported.
Journal ArticleDOI
The elitist non-dominated sorting genetic algorithm with inheritance (i-NSGA-II) and its jumping gene adaptations for multi-objective optimization
Mithilesh Kumar,Chandan Guria +1 more
TL;DR: The inclusion of the inheritance operator improves the speed of convergence to global Pareto-optimal front significantly with a minimum number of generations over existing NSGA-II and several JG adapted NSGA, and is established by solving real-life robust multi-objective optimization problems involving the drilling of oil-well and synthesis of sal oil biodiesel.
Journal ArticleDOI
An adaptive decoding biased random key genetic algorithm for cloud workflow scheduling
TL;DR: Wang et al. as mentioned in this paper proposed a novel adaptive decoding biased random key genetic algorithm for cloud workflow scheduling, where the improved real number coding based on random key with limited value range is employed, and some novel schemes such as the population initialization based on level and heuristics including dynamic heterogeneous earliest finish time, the dynamic adaptive decoding, the load balance with communication avoidance and iterative forward-backward scheduling are designed for population initialization, chromosome decoding and improvement.
Journal ArticleDOI
Research on Intelligent Welding Robot Path Optimization Based on GA and PSO Algorithms
TL;DR: Two intelligent algorithms for welding path optimization, genetic algorithm (GA) and discrete particle swarm optimization, are proposed to optimize the welding robot path and the GA achieves the fastest iterative efficiency.
Journal ArticleDOI
Application and performance of machine learning techniques in manufacturing sector from the past two decades: A review
TL;DR: Among several machine learning techniques reviewed in this study, application of artificial neural networks (ANN) in process modelling and optimization has become quite noticeable because of its ability to predict the output quickly and accurately.
References
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Dissertation
Optimization of Electrical Discharge Machining Parameters Using Genetic Algorithm Technique
TL;DR: In this article, the authors considered the material removal rate (MRR) during the process as productivity estimate with the aim to maximize it, with an intention of minimizing surface roughness is taken as most important output parameter.