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A Genetic Local Search Algorithm for Solving Symmetric and Asymmetric Traveling Salesman Problems
 In Proceedings of the 1996 IEEE International Conference on Evolutionary Computation
, 1996
"... The combination of local search heuristics and genetic algorithms is a promising approach for finding nearoptimum solutions to the traveling salesman problem (TSP). In this paper, an approach is presented in which local search techniques are used to find local optima in a given TSP search space, and ..."
Abstract

Cited by 77 (12 self)
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The combination of local search heuristics and genetic algorithms is a promising approach for finding nearoptimum solutions to the traveling salesman problem (TSP). In this paper, an approach is presented in which local search techniques are used to find local optima in a given TSP search space, and genetic algorithms are used to search the space of local optima in order to find the global optimum. New genetic operators for realizing the proposed approach are described, and the quality and efficiency of the solutions obtained for a set of symmetric and asymmetric TSP instances are discussed. The results indicate that it is possible to arrive at high quality solutions in reasonable time. I. Introduction In the Traveling Salesman Problem (TSP) [18], [27], a number of cities with distances between them is given and the task is to find the minimumlength closed tour that visits each city once and returns to its starting point. A symmetric TSP (STSP) is one where the distance between any...
Genetic Local Search for the TSP: New Results
 In Proceedings of the 1997 IEEE International Conference on Evolutionary Computation
, 1997
"... The combination of local search heuristics and genetic algorithms has been shown to be an effective approach for finding nearoptimum solutions to the traveling salesman problem. In this paper, previously proposed genetic local search algorithms for the symmetric and asymmetric traveling salesman pr ..."
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Cited by 74 (13 self)
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The combination of local search heuristics and genetic algorithms has been shown to be an effective approach for finding nearoptimum solutions to the traveling salesman problem. In this paper, previously proposed genetic local search algorithms for the symmetric and asymmetric traveling salesman problem are revisited and potential improvements are identified. Since local search is the central component in which most of the computation time is spent, improving the efficiency of the local search operators is crucial for improving the overall performance of the algorithms. The modifications of the algorithms are described and the new results obtained are presented. The results indicate that the improved algorithms are able to arrive at better solutions in significantly less time. I. Introduction Consider a salesman who wants to start from his home city, visit each of a set of n cities exactly once, and then return home. Since the salesman is interested in finding the shortest possible r...
New Genetic Local Search Operators for the Traveling Salesman Problem
, 1996
"... Abstract. In this paper, an approach is presented to incorporate problem speci c knowledge into a genetic algorithm which is used to compute nearoptimum solutions to traveling salesman problems (TSP). The approach is based on using a tour construction heuristic for generating the initial population ..."
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Cited by 54 (11 self)
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Abstract. In this paper, an approach is presented to incorporate problem speci c knowledge into a genetic algorithm which is used to compute nearoptimum solutions to traveling salesman problems (TSP). The approach is based on using a tour construction heuristic for generating the initial population, a tour improvement heuristic for nding local optima in a given TSP search space, and new genetic operators for e ectively searching the space of local optima in order to nd the global optimum. The quality and e ciency of solutions obtained for a set of TSP instances containing between 318 and 1400 cities are presented. 1
An efficient selforganizing map designed by genetic algorithms for the traveling salesman problem
 IEEE Trans. SMC Part B
, 2003
"... As a typical combinatorial optimization problem, the Traveling Salesman Problem (TSP) has attracted extensive research interest. In this paper, we develop a SelfOrganizing Map (SOM) with a novel learning rule. It is called the Integrated SOM (ISOM) since its learning rule integrates the three learn ..."
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Cited by 6 (2 self)
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As a typical combinatorial optimization problem, the Traveling Salesman Problem (TSP) has attracted extensive research interest. In this paper, we develop a SelfOrganizing Map (SOM) with a novel learning rule. It is called the Integrated SOM (ISOM) since its learning rule integrates the three learning mechanisms in the SOM literature. Within a single learning step, the excited neuron is first dragged towards the input city, then pushed to the convex hull of the TSP, and finally drawn towards the middle point of its two neighboring neurons. A genetic algorithm is successfully specified to determine the elaborate coordination among the three learning mechanisms as well as the suitable parameter setting. The evolved ISOM (eISOM) is examined on three sets of TSPs to demonstrate its power and efficiency. The computation complexity of the eISOM is quadratic, which is comparable to other SOMlike neural networks. Moreover, the eISOM can generate more accurate solutions than several typical approaches for TSPs including the SOM developed by Budinich, the expanding SOM, the convex elastic net, and the FLEXMAP algorithm. Though its solution accuracy is not yet comparable to some sophisticated heuristics, the eISOM is one of the most accurate neural networks for the TSP.
A study of five parallel approaches to a genetic algorithm for the traveling salesman problem
, 2005
"... ..."
Genetic Local Search for Job Shop Scheduling Problem
 MASTER THESIS, POLITECNICO DI
"... The Job Shop Scheduling Problem is a strongly NPhard problem of combinatorial optimisation and one of the bestknown machine scheduling problem. Taboo Search is an effective local search algorithm for the job shop scheduling problem, but the quality of the best solution found depends on the initial ..."
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Cited by 2 (1 self)
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The Job Shop Scheduling Problem is a strongly NPhard problem of combinatorial optimisation and one of the bestknown machine scheduling problem. Taboo Search is an effective local search algorithm for the job shop scheduling problem, but the quality of the best solution found depends on the initial solution. To overcome this problem we present a new approach that uses a population of Taboo Search runs in a Genetic Algorithm framework: GAs localise good areas of the solution space so that TS can start its search with promising initial solutions. The peculiarity of the Genetic Algorithm we propose consists in a natural representation which covers all and only the feasible solution space and guarantees the transmission of meaningful characteristics. The results show that this method outperforms many others producing good quality solutions in less time.
Cooperating populations  Improving the performance of Parallel Genetic Algorithms with Cooperating populations with different evolution behaviours
"... A framework for unifying Simple and Parallel Genetic Algorithm implementations as Cooperating Populations is presented. Using this framework, a method called Cooperating Populations with Different Evolution Behaviours (CoPDEB), for generalizing and improving the performance of Parallel Genetic Alg ..."
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A framework for unifying Simple and Parallel Genetic Algorithm implementations as Cooperating Populations is presented. Using this framework, a method called Cooperating Populations with Different Evolution Behaviours (CoPDEB), for generalizing and improving the performance of Parallel Genetic Algorithms (PGAs) is also presented. The main idea of CoPDEB is to maintain a number of populations exhibiting different evolution behaviours. CoPDEB was tested on three problems (the optimization of a real function, the TSP problem and the problem of training a Recurrent Artificial Neural Network), and appears to significantly increase the problemsolving capabilities over PGAs with the same evolution behaviour on each population. This paper also studies the effect of the migration rate (Epoch) and the population size on the performance of both PGAs and CoPDEB. Keywords: Parallel genetic algorithms, cooperating populations, different evolution behaviour, epoch. 1. Introduction GAs are stoc...
Fitness Landscapes of Combinatorial Problems And The Performance Of Search Algorithms
, 1997
"... This work settles in the framework of the fitness landscape paradigm and its application to combinatorial problems. We study the interdependance of fitness landscapes and heuristics, and figure out some TSP landscapes statistical characteristics associated with local search operators. Then we use ..."
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This work settles in the framework of the fitness landscape paradigm and its application to combinatorial problems. We study the interdependance of fitness landscapes and heuristics, and figure out some TSP landscapes statistical characteristics associated with local search operators. Then we use this knowledge to shed some light on the general behavior of TSP search algorithms, and derive a simple populationbased local search heuristic that performs favorably compared to the wellknown ILK technique. 1 Introduction One of the mysteries surrounding the TSP is the remarkably effective performance of simple heuristic solution methods. R. M. Karp & J. M. Steele, in [LLKS85] As stressed by this quote, one is surprised when running a simple hillclimbing algorithm on TSP instances to find, after a very short amount of time, a rather good local optimum (if using a good operator, such as the 2change). In the same time, more complex descent algorithms, such as genetic algorithms, ...
Trademarks
"... A computer based simulation with artificial adaptive agents for predicting secondary structure from the protein hydrophobicity ffl [34] Accurate Gen. Lander: An Experiment in ffl NeuroGenetic Cntr. [149] adaptation Characterizing the ffl abilities of a class of gen. based machine learning alg. [45] ..."
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A computer based simulation with artificial adaptive agents for predicting secondary structure from the protein hydrophobicity ffl [34] Accurate Gen. Lander: An Experiment in ffl NeuroGenetic Cntr. [149] adaptation Characterizing the ffl abilities of a class of gen. based machine learning alg. [45] ffl in dynamic environments through a minimal probability of exploration [125] adaptive A computer based simulation with artificial ffl agents for predicting secondary structure from the protein hydrophobicity [Abstract] [15]  A hierarchical classifier syst. implementing a motivationally autonomous ffl animat [40]  Computer simulations of ffl behavior in animats [98]  Darwinian ffl simulated annealing [99]  Extended classifiers for simulation of ffl behavior [114]  From animals to animats: everything you wanted to know about the simulation of ffl behaviour [19]  Fuzzy Qlearning and evol. strategy for ffl fuzzy cntr. [115]  Simulation of ffl behavior in animats: Review and pr...
International Journal of Management Science and Engineering Management
, 2007
"... Abstract. A Memetic Algorithm (MA) encompasses a class of approaches with proven practical success in variety of optimization problems aiming to make use of benefits of each individual approach. In this paper, we present a special designed Memetic Algorithm to solve the wellknown Symmetric Travelin ..."
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Abstract. A Memetic Algorithm (MA) encompasses a class of approaches with proven practical success in variety of optimization problems aiming to make use of benefits of each individual approach. In this paper, we present a special designed Memetic Algorithm to solve the wellknown Symmetric Traveling Salesman Problem (STSP).The main feature of the Memetic Algorithm is to use a local search combined with a special designed genetic algorithm to focus on the population of local optima. To check the performance quality of the proposed Memetic Algorithm, some benchmark problems are solved. Experiments on the benchmark set indicate that the Memetic Algorithm is quite efficient and competitive and produces good convergence behaviour and solutions.