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A Genetic Algorithm Tutorial
 Statistics and Computing
, 1994
"... This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search byhyperplane sampling. The theoretical foundations of genetic algorit ..."
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Cited by 231 (5 self)
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This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search byhyperplane sampling. The theoretical foundations of genetic algorithms are reviewed, include the schema theorem as well as recently developed exact models of the canonical genetic algorithm.
Niching Methods for Genetic Algorithms
, 1995
"... Niching methods extend genetic algorithms to domains that require the location and maintenance of multiple solutions. Such domains include classification and machine learning, multimodal function optimization, multiobjective function optimization, and simulation of complex and adaptive systems. This ..."
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Cited by 191 (1 self)
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Niching methods extend genetic algorithms to domains that require the location and maintenance of multiple solutions. Such domains include classification and machine learning, multimodal function optimization, multiobjective function optimization, and simulation of complex and adaptive systems. This study presents a comprehensive treatment of niching methods and the related topic of population diversity. Its purpose is to analyze existing niching methods and to design improved niching methods. To achieve this purpose, it first develops a general framework for the modelling of niching methods, and then applies this framework to construct models of individual niching methods, specifically crowding and sharing methods. Using a constructed model of crowding, this study determines why crowding methods over the last two decades have not made effective niching methods. A series of tests and design modifications results in the development of a highly effective form of crowding, called determin...
A Survey of Parallel Genetic Algorithms
 CALCULATEURS PARALLELES, RESEAUX ET SYSTEMS REPARTIS
, 1998
"... Genetic algorithms (GAs) are powerful search techniques that are used successfully to solve problems in many different disciplines. Parallel GAs are particularly easy to implement and promise substantial gains in performance. As such, there has been extensive research in this field. This survey att ..."
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Cited by 147 (5 self)
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Genetic algorithms (GAs) are powerful search techniques that are used successfully to solve problems in many different disciplines. Parallel GAs are particularly easy to implement and promise substantial gains in performance. As such, there has been extensive research in this field. This survey attempts to collect, organize, and present in a unified way some of the most representative publications on parallel genetic algorithms. To organize the literature, the paper presents a categorization of the techniques used to parallelize GAs, and shows examples of all of them. However, since the majority of the research in this field has concentrated on parallel GAs with multiple populations, the survey focuses on this type of algorithms. Also, the paper describes some of the most significant problems in modeling and designing multipopulation parallel GAs and presents some recent advancements.
A Parallel Genetic Algorithm for the Set Partitioning Problem
, 1994
"... In this dissertation we report on our efforts to develop a parallel genetic algorithm and apply it to the solution of the set partitioning problema difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. We developed a distributed stea ..."
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Cited by 66 (1 self)
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In this dissertation we report on our efforts to develop a parallel genetic algorithm and apply it to the solution of the set partitioning problema difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. We developed a distributed steadystate genetic algorithm in conjunction with a specialized local search heuristic for solving the set partitioning problem. The genetic algorithm is based on an island model where multiple independent subpopulations each run a steadystate genetic algorithm on their own subpopulation and occasionally fit strings migrate between the subpopulations. Tests on forty realworld set partitioning problems were carried out on up to 128 nodes of an IBM SP1 parallel computer. We found that performance, as measured by the quality of the solution found and the iteration on which it was found, improved as additional subpopulations were added to the computation. With larger numbers of subpopulations the genetic algorithm was regularly able to find the optimal solution to problems having up to a few thousand integer variables. In two cases, highquality integer feasible solutions were found for problems with 36,699 and 43,749 integer variables, respectively. A notable limitation we found was the difficulty solving problems with many constraints.
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 Overview of Evolutionary Algorithms: Practical Issues and Common Pitfalls
 Information and Software Technology
, 2001
"... An overview of evolutionary algorithms is presented covering genetic algorithms, evolution strategies, genetic programming and evolutionary programming. The schema theorem is reviewed and critiqued. Gray codes, bit representations and realvalued representations are discussed for parameter optimi ..."
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Cited by 34 (0 self)
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An overview of evolutionary algorithms is presented covering genetic algorithms, evolution strategies, genetic programming and evolutionary programming. The schema theorem is reviewed and critiqued. Gray codes, bit representations and realvalued representations are discussed for parameter optimization problems. Parallel Island models are also reviewed, and the evaluation of evolutionary algorithms is discussed.
The Island Model Genetic Algorithm: On Separability, Population Size and Convergence
 Journal of Computing and Information Technology
, 1998
"... Parallel Genetic Algorithms have often been reported to yield better performance than Genetic Algorithms which use a single large panmictic population. In the case of the Island Model genetic algorithm, it has been informally argued that having multiple subpopulations helps to preserve genetic di ..."
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Cited by 33 (0 self)
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Parallel Genetic Algorithms have often been reported to yield better performance than Genetic Algorithms which use a single large panmictic population. In the case of the Island Model genetic algorithm, it has been informally argued that having multiple subpopulations helps to preserve genetic diversity, since each island can potentially follow a different search trajectory through the search space. It is also possible that since linearly separable problems are often used to test Genetic Algorithms, that Island Models may simply be particularly well suited to exploiting the separable nature of the test problems. We explore this possibility by using the infinite population models of simple genetic algorithms to study how Island Models can track multiple search trajectories. We also introduce a simple model for better understanding when Island Model genetic algorithms may have an advantage when processing some test problems. We provide empirical results for both linearly separa...
Control of Stationary Behavior in Probabilistic Boolean Networks by Means of Structural Intervention
 Biological Systems
, 2002
"... Probabilistic Boolean Networks (PBNs) were recently introduced as mod els of gene regulatory networks. The dynamical behavior of PBNs, which are probabilistic generalizations of Boolean networks, can be studied using Markov chain theory. In particular, the steadystate or longrun behavior of PBNs ..."
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Cited by 30 (11 self)
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Probabilistic Boolean Networks (PBNs) were recently introduced as mod els of gene regulatory networks. The dynamical behavior of PBNs, which are probabilistic generalizations of Boolean networks, can be studied using Markov chain theory. In particular, the steadystate or longrun behavior of PBNs may reflect the phenotype or functional state of the cell. Approaches to alter the steadystate behavior in a specific prescribed manner, in cases of aberrant cellular states, such as tumorigenesis, would be highly beneficial. This paper develops a methodology for altering the steadystate probabil ities of certain states or sets of states with minimal modifications to the underlying rulebased structure. This approach is framed as an optimization problem that we propose to solve using genetic algorithms, which are well suited for capturing the underlying structure of PBNs and are able to locate the optimal solution in a highly efficient manner. Several computer simulation experiments support the proposed methodology.
Asparagos96 and the Traveling Salesman Problem
, 1997
"... This paper describes a spatially structured evolutionary algorithm being applied to the symmetric and asymmetric traveling salesman problem (TSP). This approach shows that a genetic algorithm with high degree of isolationbydistance in combination with a simple repairing mechanism is able to find h ..."
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Cited by 23 (0 self)
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This paper describes a spatially structured evolutionary algorithm being applied to the symmetric and asymmetric traveling salesman problem (TSP). This approach shows that a genetic algorithm with high degree of isolationbydistance in combination with a simple repairing mechanism is able to find high quality solutions for the TSP. The evolutionary part of the algorithm presented differs from the original version of Asparagos [1] in the choice of the topological pattern being now a ring structure and the support of hierarchy. The application part in contrast has been revised in more depth. A new representation which we call the bidirectional array representation [2] is used for the TSP. This representation is invariant concerning the starting point of a tour and allows the realization of a kOpt move in O(1). Our crossover operator MPX is slightly modified in the sense that if there are differing edges in the parent tours, it is now guaranteed that at least one differing edge will oc...