scholarly journals Evolving Evolutionary Algorithms Using Linear Genetic Programming

2005 ◽  
Vol 13 (3) ◽  
pp. 387-410 ◽  
Author(s):  
Mihai Oltean
Keyword(s):  
Evolutionary Algorithms ◽  
Genetic Programming ◽  
Numerical Experiments ◽  
Quadratic Assignment Problem ◽  
Function Optimization ◽  
Traveling Salesman ◽  
Quadratic Assignment ◽  
Linear Genetic Programming ◽  
The Traveling Salesman Problem ◽  
Better Than

A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem and the Quadratic Assignment Problem are evolved by using the considered model. Numerical experiments show that the evolved Evolutionary Algorithms perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.

LEARNING TO COOPERATE IN SOLVING THE TRAVELING SALESMAN PROBLEM

2005 ◽  
Vol 15 (01n02) ◽  
pp. 151-162 ◽  
Author(s):  
DEHU QI ◽  
RON SUN
Keyword(s):  
Reinforcement Learning ◽  
Traveling Salesman Problem ◽  
Single Agent ◽  
Traveling Salesman ◽  
Cooperative Team ◽  
The Traveling Salesman Problem ◽  
Better Than

A cooperative team of agents may perform many tasks better than single agents. The question is how cooperation among self-interested agents should be achieved. It is important that, while we encourage cooperation among agents in a team, we maintain autonomy of individual agents as much as possible, so as to maintain flexibility and generality. This paper presents an approach based on bidding utilizing reinforcement values acquired through reinforcement learning. We tested and analyzed this approach and demonstrated that a team indeed performed better than the best single agent as well as the average of single agents.

Cantor set based neighbor generation method for permutation solution representation

2020 ◽  
Vol 39 (5) ◽  
pp. 6157-6168
Author(s):  
Melike Öztürk ◽  
Çiğdem Alabaş-Uslu
Keyword(s):  
Quadratic Assignment Problem ◽  
Recursive Algorithm ◽  
Cantor Set ◽  
Quadratic Assignment ◽  
Single Solution ◽  
Efficiency And Effectiveness ◽  
Solution Representation ◽  
Incompatible Characteristics ◽  
Generation Mechanisms ◽  
The Traveling Salesman Problem

Metaheuristics gained world-wide popularity and researchers have been studying them vigorously in the last two decades. A relatively less explored approach in the improvement of metaheuristics is to design new neighbor generation mechanisms. Neighbor generation mechanisms are very important in the success of any single solution-based heuristic since they directly guide the search. In this study, a neighbor generation mechanism called cantor-set based (CB) method for single solution-based heuristics which use permutation solution representation is described. The inspiration for CB method stems from the recursive algorithm that constructs a cantor set which is a fractal set. Three variations of CB method are discussed (CB-1, CB-2, CB-3) considering the presented design possibilities. The computational experiments are conducted by embedding the mechanisms into the classical local search and simulated annealing algorithms, separately, to test their efficiency and effectiveness by comparing them to classical swap and insertion mechanisms. The traveling salesman problem (TSP) and quadratic assignment problem (QAP) which are very different problems that have incompatible characteristics have been chosen to test the mechanisms and sets of benchmark instances with varying sizes are chosen for the comparisons. The computational tests show that CB-2 gives very favorable results for TSP and CB-1 gives favorable results for QAP which means that CB-2 is suitable for problems that have steep landscapes and CB-1 is suitable for the problems that have flat landscapes. It is observed that CB-3 is a more generalized mechanism because it gives consistently good results for both TSP and QAP instances. The best mechanism for a given instance of the both problem types outperforms the classical neighbor generation of swap and insertion in terms of effectiveness.

Local Clustering Organization (LCO) Solving a Large-Scale TSP

2005 ◽  
Vol 17 (5) ◽  
pp. 560-567 ◽  
Author(s):  
Masashi Furukawa ◽  
◽  
Michiko Watanabe ◽  
Yusuke Matsumura ◽  
◽  
...  
Keyword(s):  
Traveling Salesman Problem ◽  
Numerical Experiments ◽  
Large Scale ◽  
The Self ◽  
Traveling Salesman ◽  
Self Organizing Map ◽  
Two Cities ◽  
Local Clustering ◽  
Different Types ◽  
The Traveling Salesman Problem

The traveling salesman problem (TSP) is one of the most difficult problems that occur in different types of industrial scheduling situations. We propose a solution, involving local clustering organization (LCO), for a large-scale TSP based on the principle of the self-organizing map (SOM). Although the SOM can solve TSPs, it is not applicable to practical TSPs because the SOM references city coordinates and assigns synapses to coordinates. LCO indirectly uses the SOM principle and, instead of city coordinates, references costs between two cities, to determine the sequence of cities. We apply LCO to a large-scale TSP to determine its efficiency in numerical experiments. Results demonstrate that LCO obtains the desired solutions.

Hierarchical Solution of the Traveling Salesman Problem with Random Dyadic Tilings

2018 ◽  
Vol 17 (01) ◽  
pp. 1850003
Author(s):  
Tamás Kalmár-Nagy ◽  
Bendegúz Dezső Bak
Keyword(s):  
Traveling Salesman Problem ◽  
Optimization Algorithm ◽  
Numerical Experiments ◽  
Heuristic Approach ◽  
Traveling Salesman ◽  
Benchmark Problem ◽  
Unit Square ◽  
The Traveling Salesman Problem ◽  
American Cities ◽  
Independent Identically Distributed

We propose a hierarchical heuristic approach for solving the Traveling Salesman Problem (TSP) in the unit square. The points are partitioned with a random dyadic tiling and clusters are formed by the points located in the same tile. Each cluster is represented by its geometrical barycenter and a “coarse” TSP solution is calculated for these barycenters. Midpoints are placed at the middle of each edge in the coarse solution. Near-optimal (or optimal) minimum tours are computed for each cluster. The tours are concatenated using the midpoints yielding a solution for the original TSP. The method is tested on random TSPs (independent, identically distributed points in the unit square) up to 10,000 points as well as on a popular benchmark problem (att532 — coordinates of 532 American cities). Our solutions are 8–13% longer than the optimal ones. We also present an optimization algorithm for the partitioning to improve our solutions. This algorithm further reduces the solution errors (by several percent using 1000 iteration steps). The numerical experiments demonstrate the viability of the approach.

scholarly journals A Self-Organizing Neural Network for the Traveling Salesman Problem That Is Competitive with Simulated Annealing

1996 ◽  
Vol 8 (2) ◽  
pp. 416-424 ◽  
Author(s):  
Marco Budinich
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Unsupervised Learning ◽  
Traveling Salesman Problem ◽  
Approximate Solutions ◽  
Elastic Net ◽  
Traveling Salesman ◽  
The Traveling Salesman Problem ◽  
Better Than ◽  
Self Organizing

Unsupervised learning applied to an unstructured neural network can give approximate solutions to the traveling salesman problem. For 50 cities in the plane this algorithm performs like the elastic net of Durbin and Willshaw (1987) and it improves when increasing the number of cities to get better than simulated annealing for problems with more than 500 cities. In all the tests this algorithm requires a fraction of the time taken by simulated annealing.

Research on MPI-Based Parallel Max-Min Ant System

2012 ◽  
Vol 198-199 ◽  
pp. 1321-1326 ◽  
Author(s):  
Yu Liu ◽  
Guo Dong Wu
Keyword(s):  
Combinatorial Optimization ◽  
Numerical Experiments ◽  
Large Scale ◽  
Optimization Problems ◽  
Computation Time ◽  
Traveling Salesman ◽  
Ant System ◽  
Combinatorial Optimization Problems ◽  
The Traveling Salesman Problem

When solving large scale combinatorial optimization problems, Max-Min Ant System requires long computation time. MPI-based Parallel Max-Min Ant System described in this paper can ensure the quality of the solution, as well as reduce the computation time. Numerical experiments on the multi-node cluster system show that when solving the traveling salesman problem, MPI-based Parallel Max-Min Ant System can get better computational efficiency.

scholarly journals Estimation Models Generation using Linear Genetic Programming

2009 ◽  
Vol 12 (3) ◽  
Author(s):  
Javier Martínez Canillas ◽  
Roberto Sánchez ◽  
Benjamín Barán
Keyword(s):  
Time Series ◽  
Evolutionary Algorithms ◽  
Genetic Programming ◽  
Price Index ◽  
Consumer Price Index ◽  
Decision Rules ◽  
Time Series Forecasting ◽  
Linear Genetic Programming ◽  
Estimation Techniques ◽  
Estimation Models

The use of decision rules and estimation techniques is increasingly common for decision mak-ing. In recent years studies were conducted which applies Genetic Programming (GP) to obtainrules to make predictions. A new branch in the area of Evolutionary Algorithms (EA) is LinearGenetic Programming (LGP). LGP evolves instructions sequences of an imperative programminglanguage. This paper proposes estimation models generation for time series forecasting using LGP.The forecasting result for the Consumer Price Index (CPI) and the price of soybeans per ton showsthe potential of this new proposal.

scholarly journals A new genetic representation for quadratic assignment problem

2011 ◽  
Vol 21 (2) ◽  
pp. 225-238 ◽  
Author(s):  
Jozef Kratica ◽  
Dusan Tosic ◽  
Vladimir Filipovic ◽  
Djordje Dugosija
Keyword(s):  
Genetic Algorithm ◽  
Assignment Problem ◽  
Numerical Experiments ◽  
Quadratic Assignment Problem ◽  
Quadratic Assignment ◽  
Data Sets ◽  
Optimal Solutions ◽  
Genetic Operators ◽  
Reasonable Time ◽  
Genetic Encoding

In this paper, we propose a new genetic encoding for well known Quadratic Assignment Problem (QAP). The new encoding schemes are implemented with appropriate objective function and modified genetic operators. The numerical experiments were carried out on the standard QAPLIB data sets known from the literature. The presented results show that in all cases proposed genetic algorithm reached known optimal solutions in reasonable time.

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