Solving Pattern Nesting Problems with Genetic Algorithms Employing Task Decomposition and Contact Detection

1995 ◽  
Vol 3 (3) ◽  
pp. 239-266 ◽  
Author(s):  
Rahul Dighe ◽  
Mark J. Jakiela
Keyword(s):  
Genetic Algorithms ◽  
Contact Detection ◽  
Two Dimensional ◽  
Hierarchical Approach ◽  
Minimum Area ◽  
Task Decomposition ◽  
Genetic Search ◽  
Sample Output

A hierarchical approach for nesting two-dimensional shapes based on genetic algorithms is described. For the higher-level search, a representation that facilitates genetic search based on recombination is developed. An alternatiye to overlap computation based on assembly of polygons is used at the lower level of search. Two implementations to find minimum-area enclosures for polygons, with and without a cohstraint on the width of stock, are discussed. Sample output illustrating the effectiveness of the approach is provided.

The Nesting of Two-Dimensional Shapes Using Genetic Algorithms

1995 ◽  
Vol 209 (2) ◽  
pp. 115-124 ◽  
Author(s):  
H S Ismail ◽  
K K B Hon
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Process ◽  
Search Space ◽  
Cutting Stock ◽  
Two Dimensional ◽  
Optimal Solutions ◽  
Spatial Constraints ◽  
Global Optimal ◽  
Optimum Solution ◽  
Rapid Conversion

The general two-dimensional cutting stock problem is concerned with the optimum layout and arrangement of two-dimensional shapes within the spatial constraints imposed by the cutting stock. The main objective is to maximize the utilization of the cutting stock material. This paper presents some of the results obtained from applying a combination of genetic algorithms and heuristic approaches to the nesting of dissimilar shapes. Genetic algorithms are stochastically based optimization approaches which mimic nature's evolutionary process in finding global optimal solutions in a large search space. The paper discusses the method by which the problem is defined and represented for analysis and introduces a number of new problem-specific genetic algorithm operators that aid in the rapid conversion to an optimum solution.

scholarly journals Integrated process planning, WSPT scheduling and WSLK due-date assignment using genetic algorithms and evolutionary strategies

2017 ◽  
Vol 8 (1) ◽  
pp. 73-83
Author(s):  
Halil Ibrahim Demir ◽  
Onur Canpolat
Keyword(s):  
Genetic Algorithms ◽  
Process Planning ◽  
Random Search ◽  
Evolutionary Strategies ◽  
Integrated Process ◽  
Due Dates ◽  
Due Date ◽  
Planning And Scheduling ◽  
Due Date Assignment ◽  
Genetic Search

Process planning, scheduling and due-date assignment are three important manufacturing functions in our life. They all try to get local optima and there can be enormous loss in overall performance value if they are handled separately. That is why they should be handled concurrently. Although integrated process planning and scheduling with due date assignment problem is not addressed much in the literature, there are numerous works on integrated process planning and scheduling and many works on scheduling with due date assignment. Most of the works in the literature assign common due date for the jobs waiting and due dates are determined without taking into account of the weights of the customers. Here process planning function is integrated with weighted shortest processing times (WSPT) scheduling and weighted slack (WSLK) due date assignment. In this study unique due dates are given to each customer and important customers gets closer due dates. Integration of these three functions is tested for different levels of integration with genetic algorithms, evolutionary strategies, hybrid genetic algorithms, hybrid evolutionary strategies and random search techniques. Best combinations are found as full integration with genetic search and hybrid genetic search. Integration of these three functions provided substantial improvements in global performance.

Recursive Learning of Genetic Algorithms with Task Decomposition and Varied Rule Set

2013 ◽  
pp. 226-247
Author(s):  
Lei Fang ◽  
Sheng-Uei Guan ◽  
Haofan Zhang
Keyword(s):  
Genetic Algorithms ◽  
Classification Accuracy ◽  
Large Scale ◽  
Chromosome Length ◽  
Task Decomposition ◽  
Rule Set ◽  
Rule Number ◽  
Algorithmic Framework ◽  
Recursive Learning ◽  
Generalization Accuracy

Rule-based Genetic Algorithms (GAs) have been used in the application of pattern classification (Corcoran & Sen, 1994), but conventional GAs have weaknesses. First, the time spent on learning is long. Moreover, the classification accuracy achieved by a GA is not satisfactory. These drawbacks are due to existing undesirable features embedded in conventional GAs. The number of rules within the chromosome of a GA classifier is usually set and fixed before training and is not problem-dependent. Secondly, conventional approaches train the data in batch without considering whether decomposition solves the problem. Thirdly, when facing large-scale real-world problems, GAs cannot utilise resources efficiently, leading to premature convergence. Based on these observations, this paper develops a novel algorithmic framework that features automatic domain and task decomposition and problem-dependent chromosome length (rule number) selection to resolve these undesirable features. The proposed Recursive Learning of Genetic Algorithm with Task Decomposition and Varied Rule Set (RLGA) method is recursive and trains and evolves a team of learners using the concept of local fitness to decompose the original problem into sub-problems. RLGA performs better than GAs and other related solutions regarding training duration and generalization accuracy according to the experimental results.

Recursive Learning of Genetic Algorithms with Task Decomposition and Varied Rule Set

2011 ◽  
Vol 2 (4) ◽  
pp. 1-24 ◽  
Author(s):  
Lei Fang ◽  
Sheng-Uei Guan ◽  
Haofan Zhang
Keyword(s):  
Genetic Algorithms ◽  
Large Scale ◽  
Chromosome Length ◽  
Task Decomposition ◽  
Rule Set ◽  
Rule Number ◽  
Algorithmic Framework ◽  
Recursive Learning ◽  
Generalization Accuracy ◽  
Better Than

Rule-based Genetic Algorithms (GAs) have been used in the application of pattern classification (Corcoran & Sen, 1994), but conventional GAs have weaknesses. First, the time spent on learning is long. Moreover, the classification accuracy achieved by a GA is not satisfactory. These drawbacks are due to existing undesirable features embedded in conventional GAs. The number of rules within the chromosome of a GA classifier is usually set and fixed before training and is not problem-dependent. Secondly, conventional approaches train the data in batch without considering whether decomposition solves the problem. Thirdly, when facing large-scale real-world problems, GAs cannot utilise resources efficiently, leading to premature convergence. Based on these observations, this paper develops a novel algorithmic framework that features automatic domain and task decomposition and problem-dependent chromosome length (rule number) selection to resolve these undesirable features. The proposed Recursive Learning of Genetic Algorithm with Task Decomposition and Varied Rule Set (RLGA) method is recursive and trains and evolves a team of learners using the concept of local fitness to decompose the original problem into sub-problems. RLGA performs better than GAs and other related solutions regarding training duration and generalization accuracy according to the experimental results.

scholarly journals A Two-Dimensional Genetic Algorithm and Its Application to Aircraft Scheduling Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Ming-Wen Tsai ◽  
Tzung-Pei Hong ◽  
Woo-Tsong Lin
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Scheduling Problem ◽  
Two Dimensional ◽  
Dimensional Representation ◽  
One Dimensional ◽  
Time Table ◽  
Feasible Solutions ◽  
Crossover And Mutation ◽  
Airline Company

Genetic algorithms have become increasingly important for researchers in resolving difficult problems because they can provide feasible solutions in limited time. Using genetic algorithms to solve a problem involves first defining a representation that describes the problem states. Most previous studies have adopted one-dimensional representation. Some real problems are, however, naturally suitable to two-dimensional representation. Therefore, a two-dimensional encoding representation is designed and the traditional genetic algorithm is modified to fit the representation. Particularly, appropriate two-dimensional crossover and mutation operations are proposed to generate candidate chromosomes in the next generations. A two-dimensional repairing mechanism is also developed to adjust infeasible chromosomes to feasible ones. Finally, the proposed approach is used to solve the scheduling problem of assigning aircrafts to a time table in an airline company for demonstrating the effectiveness of the proposed genetic algorithm.

Optimization of Bicycle Frames Using Genetic Algorithms

2007 ◽  
Author(s):  
Yuan Mao Huang ◽  
Kuo Juei Wang
Keyword(s):  
Genetic Algorithms ◽  
Factor Of Safety ◽  
Two Dimensional ◽  
Optimal Solutions ◽  
Dimensional Model ◽  
Equilibrium Equations ◽  
Fitness Value ◽  
Two Dimensional Model ◽  
Bicycle Frame ◽  
The Mean

A bicycle frame is optimized for the lightest weight by using genetic algorithms in this study. Stresses of five rods in the bicycle frame less than the material yielding strength with consideration of the factor of safety are the constraints. A two-dimensional model of the frame is created. Equilibrium equations are derived and loads acting on rods are determined. A known function is used to verify feasibility of the program generated. Effects of the mutation rate, the crossover rate and the number of generation on the mean and the standard deviation of the fitness value are studied. The optimal solutions with the outer diameters and the inner diameters of the front frame rods to be 0.040 m and 0.038 m, respectively, the outer diameters and the inner diameters of the rear frame rods to be 0.024 m and 0.021m, respectively, and the weight of the bicycle frame to be 0.896 kg are recommended for the bicycle frame.

scholarly journals Genetiniai algoritmai komivojažieriaus uždaviniui: negatyvieji ir pozityvieji aspektai*

2009 ◽  
Vol 50 ◽  
pp. 173-180
Author(s):  
Alfonsas Misevičius ◽  
Andrius Blažinskas ◽  
Jonas Blonskis ◽  
Vytautas Bukšnaitis
Keyword(s):  
Genetic Algorithms ◽  
Traveling Salesman Problem ◽  
Optimization Problem ◽  
Combinatorial Optimization Problem ◽  
Traveling Salesman ◽  
High Quality ◽  
Genetic Search ◽  
Local Improvement ◽  
Effi Ciency ◽  
The Traveling Salesman Problem

Šiame straipsnyje nagrinėjami klausimai, susiję su genetinių algoritmų taikymu, sprendžiant gerai žinomą kombinatorinio optimizavimo uždavinį – komivojažieriaus uždavinį (KU) (angl. traveling salesman problem). Svarstoma, jog genetinio algoritmo efektyvumui didelę įtaką turi uždavinio specifi nės savybės, todėl labai svarbu kūrybiškai sudaryti genetinį algoritmą konkrečiam sprendžiamam uždaviniui. Pateikiami eksperimentų, atliktų su realizuotu genetiniu algoritmu, rezultatai, iliustruojantys skirtingų veiksnių įtaką rezultatų kokybei. Konstatuojama, kad tinkamas genetinių operatorių ir lokaliojo individų (sprendinių) gerinimo derinimas leidžia gerokai padidinti genetinės paieškos efektyvumą.On the Genetic Algorithms for the Traveling Salesman Problem: Negative and Positive AspectsAlfonsas Misevičius, Andrius Blažinskas, Jonas Blonskis, Vytautas Bukšnaitis SummaryIn this paper, we discuss some issues related to the application of genetic algorithms (GAs) to the well-known combinatorial optimization problem – the traveling salesman problem (TSP). The results obtained from the experiments with the different variants of the genetic algorithm are presented as well. Based on these results, it is concluded that the effi ciency of the genetic search is much infl uenced by both the specifi c nature of the problem and the features of the algorithm itself. In particular, it should be emphasized that the incorporation of the (postcrossover) procedures for the local improvement of offspring has one of the crucial roles in obtaining high-quality solutions.

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