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68
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
MPAES: A Memetic Algorithm for Multiobjective Optimization
, 2000
"... A memetic algorithm for tackling multiobjective optimization problems is presented. The algorithm employs the proven local search strategy used in the Pareto archived evolution strategy (PAES) and combines it with the use of a population and recombination. Verification of the new algorithm is carri ..."
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Cited by 52 (5 self)
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A memetic algorithm for tackling multiobjective optimization problems is presented. The algorithm employs the proven local search strategy used in the Pareto archived evolution strategy (PAES) and combines it with the use of a population and recombination. Verification of the new algorithm is carried out by testing it on a set of multiobjective 0/1 knapsack problems. On each problem instance, comparison is made between the new memetic algorithm, the (1+1)PAES local searcher, and the strength Pareto evolutionary algorithm (SPEA) of Zitzler and Thiele. 1 Introduction In recent years, genetic algorithms (GAs) have been applied more and more to multiobjective problems. For a comprehensive overview, see [2]. Undoubtedly, as an extremely general metaheuristic, GAs are well qualified to tackle problems of a great variety. This asset, coupled with the possession of a population, seems to make them particularly attractive for use in multiobjective problems, where a number of solutions appro...
Memetic Algorithms for Combinatorial Optimization Problems: Fitness Landscapes and Effective Search Strategies
, 2001
"... ..."
Fitness Landscapes, Memetic Algorithms, and Greedy Operators for Graph Bipartitioning
 Evolutionary Computation
, 2000
"... The fitness landscape of the graph bipartitioning problem is investigated by performing a search space analysis for several types of graphs. The analysis shows that the structure of the search space is significantly different for the types of instances studied. Moreover, with increasing epistasis ..."
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Cited by 48 (13 self)
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The fitness landscape of the graph bipartitioning problem is investigated by performing a search space analysis for several types of graphs. The analysis shows that the structure of the search space is significantly different for the types of instances studied. Moreover, with increasing epistasis, the amount of gene interactions in the representation of a solution in an evolutionary algorithm, the number of local minima for one type of instance decreases and, thus, the search becomes easier. We suggest that other characteristics besides high epistasis might have greater influence on the hardness of a problem. To understand these characteristics, the notion of a dependency graph describing gene interactions is introduced.
Improvements on AntSystem: Introducing MAXMIN Ant System
, 1996
"... Ant System is a general purpose heuristic algorithm inspired by the foraging behavior of real ant colonies. Here we introduce an improved version of Ant System, that we called MAXMIN Ant System. We describe the new features present in MAXMIN Ant System, make a detailed experimental investigation ..."
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Cited by 46 (7 self)
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Ant System is a general purpose heuristic algorithm inspired by the foraging behavior of real ant colonies. Here we introduce an improved version of Ant System, that we called MAXMIN Ant System. We describe the new features present in MAXMIN Ant System, make a detailed experimental investigation on the contribution of the design choices to the improved performance and give computational results for the application to symmetric and asymmetric Traveling Salesman Problems. The performance of MAXMIN Ant System can be further improved by adding a local search phase in which some ants are allowed to improve their solution.
ACO Algorithms for the Traveling Salesman Problem
 Periaux (eds), Evolutionary Algorithms in Engineering and Computer Science: Recent Advances in Genetic Algorithms, Evolution Strategies, Evolutionary Programming, Genetic Programming and Industrial Applications
, 1999
"... Ant algorithms [18, 14, 19] are a recently developed, populationbased approach which has been successfully applied to several NPhard combinatorial ..."
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Cited by 43 (6 self)
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Ant algorithms [18, 14, 19] are a recently developed, populationbased approach which has been successfully applied to several NPhard combinatorial
A Genetic Local Search Approach to the Quadratic Assignment Problem
 in Proceedings of the 7th International Conference on Genetic Algorithms
, 1997
"... Augmenting genetic algorithms with local search heuristics is a promising approach to the solution of combinatorial optimization problems. In this paper, a genetic local search approach to the quadratic assignment problem (QAP) is presented. New genetic operators for realizing the approach are descr ..."
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Cited by 38 (9 self)
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Augmenting genetic algorithms with local search heuristics is a promising approach to the solution of combinatorial optimization problems. In this paper, a genetic local search approach to the quadratic assignment problem (QAP) is presented. New genetic operators for realizing the approach are described, and its performance is tested on various QAP instances containing between 30 and 256 facilities/locations. The results indicate that the proposed algorithm is able to arrive at high quality solutions in a relatively short time limit: for the largest publicly known problem instance, a new best solution could be found. 1 INTRODUCTION In the quadratic assignment problem (QAP), n facilities have to be assigned to n locations at minimum cost. Given a set \Pi(n) of all permutations of f1; 2; : : : ; ng and two n \Theta n matrices A = (a ij ) and B = (b ij ), the task is to minimize the quantity C(ß) = n X i=1 n X j=1 a ij b ß(i)ß(j) ; ß 2 \Pi(n): (1) Matrix A can be interpreted as a ...
Evolving Objects: a general purpose evolutionary computation library
, 2001
"... This paper presents the evolving objects library (EOlib), an objectoriented framework for evolutionary computation (EC) that aims to provide a exible set of classes to build EC applications. EOlib design objective is to be able to evolve any object in which tness makes sense. ..."
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Cited by 36 (5 self)
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This paper presents the evolving objects library (EOlib), an objectoriented framework for evolutionary computation (EC) that aims to provide a exible set of classes to build EC applications. EOlib design objective is to be able to evolve any object in which tness makes sense.
Memetic Algorithms and the Fitness Landscape of the Graph BiPartitioning Problem
 in Proceedings of the 5th International Conference on Parallel Problem Solving from Nature  PPSN
, 1998
"... . In this paper, two types of fitness landscapes of the graph bipartitioning problem are analyzed, and a memetic algorithm  a genetic algorithm incorporating local search  that finds nearoptimum solutions efficiently is presented. A search space analysis reveals that the fitness landscapes of g ..."
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Cited by 33 (6 self)
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. In this paper, two types of fitness landscapes of the graph bipartitioning problem are analyzed, and a memetic algorithm  a genetic algorithm incorporating local search  that finds nearoptimum solutions efficiently is presented. A search space analysis reveals that the fitness landscapes of geometric and nongeometric random graphs differ significantly, and within each type of graph there are also differences with respect to the epistasis of the problem instances. As suggested by the analysis, the performance of the proposed memetic algorithm based on KernighanLin local search is better on problem instances with high epistasis than with low epistasis. Further analytical results indicate that a combination of a recently proposed greedy heuristic and KernighanLin local search is likely to perform well on geometric graphs. The experimental results obtained for nongeometric graphs show that the proposed memetic algorithm (MA) is superior to any other heuristic known to us. For th...