New Crossover Operator of Genetic Algorithms for the TSP

2009 ◽  
Author(s):  
Fanchen Su ◽  
Fuxi Zhu ◽  
Zhiyi Yin ◽  
Haitao Yao ◽  
Qingping Wang ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Crossover Operator

Robust confidence intervals applied to crossover operator for real-coded genetic algorithms

2007 ◽  
Vol 12 (8) ◽  
pp. 809-833 ◽  
Author(s):  
Domingo Ortiz-Boyer ◽  
César Hervás-Martínez ◽  
Nicolás García-Pedrajas
Keyword(s):  
Genetic Algorithms ◽  
Confidence Intervals ◽  
Crossover Operator ◽  
Real Coded Genetic Algorithms

scholarly journals EVOLUTIONARY OPTIMIZATION OF RBF NETWORKS

2001 ◽  
Vol 11 (03) ◽  
pp. 287-294 ◽  
Author(s):  
E. LACERDA ◽  
A. DE CARVALHO ◽  
TERESA LUDERMIR
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Multiobjective Optimization ◽  
Optimization Criterion ◽  
Benchmark Problem ◽  
Crossover Operator ◽  
Rbf Networks ◽  
New Strategy ◽  
Free Parameters

One of the main obstacles to the widespread use of artificial neural networks is the difficulty of adequately defining values for their free parameters. This article discusses how Radial Basis Function (RBF) networks can have their parameters defined by genetic algorithms. For such, it presents an overall view of the problems involved and the different approaches used to genetically optimize RBF networks. A new strategy to optimize RBF networks using genetic algorithms is proposed, which includes new representation, crossover operator and the use of a multiobjective optimization criterion. Experiments using a benchmark problem are performed and the results achieved using this model are compared to those achieved by other approaches.

A New Crossover Operator for Assembly Sequence Planning by Genetic Algorithms

1996 ◽  
Author(s):  
Milad Fares Sebaaly ◽  
Hideo Fujimoto
Keyword(s):  
Genetic Algorithms ◽  
Decision Process ◽  
Genetic Material ◽  
Assembly Sequence Planning ◽  
Assembly Sequence ◽  
Crossover Operator ◽  
Optimal Sequence ◽  
Sequence Planning ◽  
Complete Set ◽  
Maximum Transmission

Abstract Assembly Sequence Planning (ASP) is the generation of the best or optimal sequence to assemble a certain product, given its design files. Although many planners were introduced in research to solve this problem automatically, it is still solved manually in many advanced assembly firms. The reason behind this is that most introduced planners are very sensitive to large increases in product parts. In fact, most of these planners seek the exact solution, while performing a part basis decision process. As a result, they are trapped in tedious and exhaustive search procedures, which make them inefficient and sometimes obsolete. To overcome these difficulties, Sebaaly and Fujimoto (1996) introduced a new concept of ASP based on Genetic Algorithms application, where the search procedure is performed on a sequence population basis rather than a part basis, and a best sequence is generated without searching the complete set of potential candidates. This paper addresses the problem of improving the GA performance for assembly application, by introducing a new crossover operator. The genetic material can be divided and classified as ‘good’ or ‘bad’. The new crossover insures the maximum transmission of ‘good’ features from one generation to another. This results in a faster GA convergence. The performance of the new algorithm is compared with that of the ordinary matrix crossover for a modified industrial example, where it proved to be faster and more efficient.

A FITNESS-BASED MULTI-PARENT CROSSOVER OPERATOR WITH PROBABILISTIC SELECTION

2012 ◽  
Vol 21 (01) ◽  
pp. 1250005
Author(s):  
SURAPONG AUWATANAMONGKOL
Keyword(s):  
Genetic Algorithms ◽  
Threshold Value ◽  
Fast Convergence ◽  
Premature Convergence ◽  
Test Functions ◽  
Crossover Operator ◽  
Solution Quality ◽  
Early Stages ◽  
Optimal Setting ◽  
Conflicting Goals

Several multi-parent crossover operators have been proposed to increase the performance of genetic algorithms. In these cases, the operators allow several parents to simultaneously take part in creating offspring. However, the operators need to find a balance between the two conflicting goals of exploitation and exploration. Strong exploitation allows fast convergence to succeed but can lead to premature convergence while strong exploration can lead to better solution quality but slower convergence. This paper proposes a new fitness based scanning multi-parent crossover operator for genetic algorithms. The new operator seeks out the optimal setting for the two goals in order to achieve the highest benefits from both. The operator uses a probabilistic selection with an incremental threshold value to allow strong exploration in the early stages of the algorithms and strong exploitation in their later stages. Experiments conducted on some test functions show that the operator can give better solution quality and more convergence consistency when compared with some other well-known multi-parent crossover operators.

A novel ODV crossover operator-based genetic algorithms for traveling salesman problem

2020 ◽  
Vol 24 (17) ◽  
pp. 12855-12885 ◽  
Author(s):  
P. Victer Paul ◽  
C. Ganeshkumar ◽  
P. Dhavachelvan ◽  
R. Baskaran
Keyword(s):  
Genetic Algorithms ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Crossover Operator

scholarly journals Optimization of Warehouse Operations with Genetic Algorithms

2020 ◽  
Vol 10 (14) ◽  
pp. 4817
Author(s):  
Mirosław Kordos ◽  
Jan Boryczko ◽  
Marcin Blachnik ◽  
Sławomir Golak
Keyword(s):  
Genetic Algorithms ◽  
Product Placement ◽  
Order Picking ◽  
Crossover Operator ◽  
Proper Number ◽  
Random Product ◽  
Fully Automatic ◽  
The Cost ◽  
The Given ◽  
Warehouse Operations

We present a complete, fully automatic solution based on genetic algorithms for the optimization of discrete product placement and of order picking routes in a warehouse. The solution takes as input the warehouse structure and the list of orders and returns the optimized product placement, which minimizes the sum of the order picking times. The order picking routes are optimized mostly by genetic algorithms with multi-parent crossover operator, but for some cases also permutations and local search methods can be used. The product placement is optimized by another genetic algorithm, where the sum of the lengths of the optimized order picking routes is used as the cost of the given product placement. We present several ideas, which improve and accelerate the optimization, as the proper number of parents in crossover, the caching procedure, multiple restart and order grouping. In the presented experiments, in comparison with the random product placement and random product picking order, the optimization of order picking routes allowed the decrease of the total order picking times to 54%, optimization of product placement with the basic version of the method allowed to reduce that time to 26% and optimization of product placement with the methods with the improvements, as multiple restart and multi-parent crossover to 21%.

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