Reducing execution time on genetic algorithm in real-world applications using fitness prediction: parameter optimization of SRM control

Advanced SIMION Techniques: Boundary Matching and Genetic Optimization

Microscopy and Microanalysis ◽

10.1017/s1431927615013409 ◽

2015 ◽

Vol 21 (S4) ◽

pp. 218-223 ◽

Cited By ~ 1

Author(s):

D. Dowsett

Keyword(s):

Genetic Algorithm ◽

Real World ◽

Great Accuracy ◽

Genetic Optimization ◽

Real World Applications ◽

Boundary Matching

AbstractTwo techniques for use with SIMION [1] are presented, boundary matching and genetic optimization. The first allows systems which were previously difficult or impossible to simulate in SIMION to be simulated with great accuracy. The second allows any system to be rapidly and robustly optimized using a parallelized genetic algorithm. Each method will be described along with examples of real world applications.

Download Full-text

Efficient Mining of Partial Periodic Patterns with Individual Event Support Thresholds Using Minimum Constraints

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488514500421 ◽

2014 ◽

Vol 22 (06) ◽

pp. 793-814

Author(s):

Kung-Jiuan Yang ◽

Tzung-Pei Hong ◽

Guo-Cheng Lan ◽

Yuh-Min Chen

Keyword(s):

Real World ◽

Execution Time ◽

Real Life ◽

Individual Event ◽

Periodic Patterns ◽

Minimum Support ◽

Real World Applications ◽

Support Threshold ◽

Multiple Minimum Supports ◽

Minimum Constraints

Partial periodic patterns are commonly seen in real-life applications and provide useful prediction with uncertainty. Most previous approaches have set a single minimum support threshold for all events to assume they have similar frequencies which is not practical for real-world applications. Instead of setting a single minimum support threshold for all events, Chen et al. proposed an FP-tree-like algorithm to allow multiple minimum supports for reflecting the natures of the events. However, such a tree-based algorithm encountered an efficiency problem while period length is long or event sequential orders in period segments are varied. Under the circumstance, many tree branches are created and much execution time is spent to find partial periodic patterns. In this paper, we thus propose a projection-based algorithm which examines only prefix subsequences and projects only corresponding postfix subsequences with multiple minimum supports to quickly find the partial periodic patterns in a recursive process. Experiments on both synthetic and real-life datasets show that the proposed algorithm is more efficient than the previous one.

Download Full-text

A Hybrid Genetic Algorithm for Pallet Loading in Real-World Applications

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2020.12.2719 ◽

2020 ◽

Vol 53 (2) ◽

pp. 10006-10010

Author(s):

Gabriele Ancora ◽

Gianluca Palli ◽

Claudio Melchiorri

Keyword(s):

Genetic Algorithm ◽

Real World ◽

Hybrid Genetic Algorithm ◽

Pallet Loading ◽

Real World Applications

Download Full-text

Intelligent Dynamic Production Scheduling in High-Mix Low-Volume Manufacturing Systems Under Uncertain Environment

Engineering/Technology Management ◽

10.1115/imece2005-82415 ◽

2005 ◽

Author(s):

Sk Ahad Ali ◽

Hamid Seifoddini ◽

Hong Sun

Keyword(s):

Genetic Algorithm ◽

Real World ◽

Production Scheduling ◽

Manufacturing Systems ◽

Lean Production ◽

Simulation Environment ◽

Real World Applications ◽

Key Factor ◽

Low Volume ◽

Dynamic Production

Today’s globalization market drives industries toward increased expectations on lean production. These expectations have put industries under pressure to become more agile under highly dynamic market and manufacturing conditions in the high-mix low-volume manufacturing systems. Dynamic production scheduling is a key factor in fulfilling the customer’s expectation. It becomes more critical due to dynamics and uncertainty in the manufacturing systems. This research addresses the uncertainty consideration of machine and labor for dynamic production scheduling. Fuzzy based system is used to capture the labor and machine uncertainty and implemented in simulation environment. Based on the variability from the simulation environment, a genetic algorithm based optimization tool is developed for dynamic production scheduling. The proposed method is validated with real-world applications.

Download Full-text

Special Section: Evolutionary Design

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s0890060499135005 ◽

1999 ◽

Vol 13 (5) ◽

pp. 325-325

Author(s):

PETER BENTLEY

Keyword(s):

Genetic Algorithm ◽

Recent Work ◽

Real World ◽

Special Section ◽

Computation Time ◽

Evolutionary Design ◽

Michigan State University ◽

State University ◽

Design Problems ◽

Real World Applications

This issue of AIEDAM is the second in a series of three “mini” special issues on Evolutionary Design by computers. The papers continue the theme that began in Vol. 13, No. 3, 1999, of using Evolutionary Computation for design problems. The first paper by Eby, Averill, Punch and Goodman provides an excellent overview of the most recent work at Michigan State University on this subject. They describe their work on the optimization of flywheels by an injection island genetic algorithm, and show the importance of minimizing the computation time devoted to evaluation for such real-world applications.

Download Full-text