Recognition of Machining Features and Optimum Process Plan Generation using Genetic Algorithms

2008 ◽  
Vol 9 (3-4) ◽  
pp. 145-158
Author(s):  
D. Sreeramulu, ◽  
C.S.P. Rao,
Keyword(s):  
Genetic Algorithms ◽  
Optimum Process ◽  
Machining Features ◽  
Process Plan ◽  
Plan Generation

scholarly journals Strategic Team AI Path Plans: Probabilistic Pathfinding

2008 ◽  
Vol 2008 ◽  
pp. 1-6 ◽  
Author(s):  
Tng C. H. John ◽  
Edmond C. Prakash ◽  
Narendra S. Chaudhari
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Computer Games ◽  
Fitness Function ◽  
New Method ◽  
High Quality ◽  
Plan Generation ◽  
Novel Method ◽  
Games And Simulations

This paper proposes a novel method to generate strategic team AI pathfinding plans for computer games and simulations using probabilistic pathfinding. This method is inspired by genetic algorithms (Russell and Norvig, 2002), in that, a fitness function is used to test the quality of the path plans. The method generates high-quality path plans by eliminating the low-quality ones. The path plans are generated by probabilistic pathfinding, and the elimination is done by a fitness test of the path plans. This path plan generation method has the ability to generate variation or different high-quality paths, which is desired for games to increase replay values. This work is an extension of our earlier work on team AI: probabilistic pathfinding (John et al., 2006). We explore ways to combine probabilistic pathfinding and genetic algorithm to create a new method to generate strategic team AI pathfinding plans.

Sustainable Process Plan Generation in RMS: A Comparative Study of Two Multi-objective Evolutionary Approaches

2021 ◽  
pp. 329-339
Author(s):  
Imen Khettabi ◽  
Lyes Benyoucef ◽  
Mohamed Amine Boutiche
Keyword(s):  
Comparative Study ◽  
Process Plan ◽  
Multi Objective ◽  
Plan Generation
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