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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 55 (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...
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
Guided local search and its application to the traveling salesman problem
, 1999
"... The Traveling Salesman Problem (TSP) is one of the most famous problems in combinatorial optimization. In this paper, we are going to examine how the techniques of Guided Local Search (GLS) and Fast Local Search (FLS) can be applied to the problem. GLS sits on top of local search heuristics and has ..."
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Cited by 52 (16 self)
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The Traveling Salesman Problem (TSP) is one of the most famous problems in combinatorial optimization. In this paper, we are going to examine how the techniques of Guided Local Search (GLS) and Fast Local Search (FLS) can be applied to the problem. GLS sits on top of local search heuristics and has as a main aim to guide these procedures in exploring efficiently and effectively the vast search spaces of combinatorial optimization problems. GLS can be combined with the neighborhood reduction scheme of FLS which significantly speeds up the operations of the algorithm. The combination of GLS and FLS with TSP local search heuristics of different efficiency and effectiveness is studied in an effort to determine the dependence of GLS on the underlying local search heuristic used. Comparisons are made with some of the best TSP heuristic algorithms and general optimization techniques which demonstrate the advantages of GLS over alternative heuristic approaches suggested for the problem.
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 48 (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.
Memetic Algorithms for Combinatorial Optimization Problems: Fitness Landscapes and Effective Search Strategies
, 2001
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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 46 (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.
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 40 (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 37 (6 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.