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23
Tackling RealCoded Genetic Algorithms: Operators and Tools for Behavioural Analysis
 Artificial Intelligence Review
, 1998
"... . Genetic algorithms play a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution principles of ..."
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Cited by 123 (24 self)
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. Genetic algorithms play a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution principles of populations. These algorithms process a population of chromosomes, which represent search space solutions, with three operations: selection, crossover and mutation. Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with these algorithms do not stem from the use of this alphabet; other coding types have been considered for the representation issue, such as real coding, which would seem particularly natural when tackling optimization problems of parameters with variables in continuous domains. In this paper we review the features of realcoded genetic algorithms. Different models of genetic operators and some me...
An Integrated Technique for Task Matching and Scheduling onto Distributed Heterogeneous Computing Systems
 J. of Par. and Dist. Comp
, 2002
"... This paper presents a problemspace genetic algorithm (PSGA)based technique for efficient matching and scheduling of an application program that can be represented by a directed acyclic graph, onto a mixedmachine distributed heterogeneous computing (DHC) system.PSGA is an evolutionary technique th ..."
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Cited by 22 (0 self)
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This paper presents a problemspace genetic algorithm (PSGA)based technique for efficient matching and scheduling of an application program that can be represented by a directed acyclic graph, onto a mixedmachine distributed heterogeneous computing (DHC) system.PSGA is an evolutionary technique that combines the search capability of genetic algorithms with a known fast problemspecific heuristic to provide the bestpossible solution to a problem in an efficient manner as compared to other probabilistic techniques. The goal of the algorithm is to reduce the overall completion time through proper task matching, task scheduling, and intermachine data transfer scheduling in an integrated fashion.The algorithm is based on a new evolutionary technique that embeds a known problemspecific fast heuristic into genetic algorithms (GAs).The algorithm is robust in the sense that it explores a large and complex solution space in smaller CPU time and uses less memory space as compared to traditional GAs.Consequently, the proposed technique schedules an application program with a comparable schedule length in a very short CPU time, as compared to GAbased heuristics.The paper includes a performance comparison showing the viability and effectiveness of the proposed technique through comparison with existing
Decomposing Bayesian Networks: Triangulation of Moral Graph with Genetic Algorithms
 Statistics and Computing
, 1997
"... In this paper we consider the optimal decomposition of Bayesian networks. More concretely, we examine  empirically , the applicability of genetic algorithms to the problem of the triangulation of moral graphs. This problem constitutes the only difficult step in the evidence propagation algorithm ..."
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Cited by 22 (4 self)
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In this paper we consider the optimal decomposition of Bayesian networks. More concretely, we examine  empirically , the applicability of genetic algorithms to the problem of the triangulation of moral graphs. This problem constitutes the only difficult step in the evidence propagation algorithm of Lauritzen and Spiegelhalter (1988) and is known to be NPhard (Wen, 1991). We carry out experiments with distinct crossover and mutation operators and with different population sizes, mutation rates and selection biasses. The results are analyzed statistically. They turn out to improve the results obtained with most other known triangulation methods (Kjaerulff, 1990) and are comparable to the ones obtained with simulated annealing (Kjaerulff, 1990; Kjaerulff, 1992). Keywords: Bayesian networks, genetic algorithms, optimal decomposition, graph triangulation, moral graph, NPhard problems, statistical analysis. 1 Introduction The Bayesian networks constitute a reasoning method based on p...
Static Mapping Heuristics for Tasks with Dependencies, Priorities, Deadlines, and . . .
, 2002
"... Heterogeneous computing (HC) environments composed of interconnected machines with varied computational capabilities are well suited to meet the computational demands of large, diverse groups of tasks. The problem of mapping (defined as matching and scheduling) these tasks onto the machines of a di ..."
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Cited by 14 (6 self)
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Heterogeneous computing (HC) environments composed of interconnected machines with varied computational capabilities are well suited to meet the computational demands of large, diverse groups of tasks. The problem of mapping (defined as matching and scheduling) these tasks onto the machines of a distributed HC environment has been shown, in general, to be NPcomplete. Therefore, the development of heuristic techniques to find nearoptimal solutions is required. In the HC environment investigated, tasks had deadlines, priorities, multiple versions, and may be composed of communicating subtasks. The best static (o#line) techniques from some previous studies were adapted and applied to this mapping problem: a genetic algorithm (GA), a GENITORstyle algorithm, and a greedy Minmin technique. Simulation studies compared the performance of these heuristics in several overloaded scenarios, i.e., not all tasks executed. The performance measure used was a sum of weighted priorities of tasks that completed before their deadline, adjusted based on the version of the task used. It is shown that for the cases studied here, the GENITOR technique found the best results, but the faster Minmin approach also performed very well.
Modeling Simple Genetic Algorithms for Permutation Problems
 in Foundations of Genetic Algorithms
, 1995
"... An exact model of a simple genetic algorithm is developed for permutation based representations. Permutation based representations are used for scheduling problems and combinatorial problems such as the Traveling Salesman Problem. A remapping function is developed to remap the model to all permut ..."
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Cited by 9 (1 self)
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An exact model of a simple genetic algorithm is developed for permutation based representations. Permutation based representations are used for scheduling problems and combinatorial problems such as the Traveling Salesman Problem. A remapping function is developed to remap the model to all permutations in the search space. The mixing matrices for various permutation based operators are also developed.
Genetic Algorithms for Cutting Stock Problems: with and without Contiguity
, 1994
"... A number of optimisation problems involve the optimal grouping of a finite set of items into a number of categories subject to one or more constraints. Such problems raise interesting issues in mapping solutions in genetic algorithms. These problems range from the knapsack problem to bin packing and ..."
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Cited by 8 (0 self)
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A number of optimisation problems involve the optimal grouping of a finite set of items into a number of categories subject to one or more constraints. Such problems raise interesting issues in mapping solutions in genetic algorithms. These problems range from the knapsack problem to bin packing and cutting stock problems. This paper describes research involving cutting stock problems. Results show that the mapping that is used affects the solution in terms of both quality of the solution found and time taken to find solutions, and that different mappings are suitable for different variants of the problem. 1. Introduction Genetic algorithms(GAs) have been applied to a number of ordering problems, ranging from the Travelling Salesperson Problem (Goldberg 1985) to Jobshop Scheduling (Davis 1985). Typically these problems have been mapped by representing the solutions as ordered lists of alleles, each allele representing an item. Falkenauer (1991) has defined a subset of these problem...
Comparing Heuristic, Evolutionary and Local Search Approaches to Scheduling
 Proceedings of the Third International Conference on Artificial Intelligence Planning Systems, Menlo Park, CA
, 1996
"... The choice of search algorithm can play a vital role in the success of a scheduling application. In this paper, we investigate the contribution of search algorithms in solving a realworld warehouse scheduling problem. We compare performance of three types of scheduling algorithms: heuristic, gen ..."
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Cited by 8 (1 self)
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The choice of search algorithm can play a vital role in the success of a scheduling application. In this paper, we investigate the contribution of search algorithms in solving a realworld warehouse scheduling problem. We compare performance of three types of scheduling algorithms: heuristic, genetic algorithms and local search.
A Genetic Algorithm for Stock Cutting: An Exploration of Mapping Schemes
, 1993
"... A number of optimisation problems involve the optimal grouping of a finite set of items into a number of categories. Such problems raise interesting issues in mapping solutions to strings in genetic algorithms. We argue that single string representation of solutions as permuted lists for such proble ..."
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Cited by 6 (3 self)
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A number of optimisation problems involve the optimal grouping of a finite set of items into a number of categories. Such problems raise interesting issues in mapping solutions to strings in genetic algorithms. We argue that single string representation of solutions as permuted lists for such problems is limiting. This paper describes research into mapping a stock cutting problem where a set number of rods must be cut from a number of stock lengths with minimum wastage. Alternative mapping strategies are described, and the results of a successful implementation using a twochromosome genetic algorithm with one groupbased chromosome and one permuted list chromosome are presented. We conclude that this problem could be successfully implemented in a singlechromosome grouping genetic algorithm, although more complex grouping problems would require multichromosomes. 1. Introduction Genetic algorithms (GAs) describe a relatively new class of optimisation methods, loosely based on Darwini...
Hybrid evolutionary algorithms and clustering search
 IN: CRINA GROSAN,AJITH ABRAHAM AND HISAO ISHIBUCHI (EDS) HYBRID EVOLUTIONARY SYSTEMS  STUDIES IN COMPUTATIONAL INTELLIGENCE  SPRINGER SCI SERIES. 75 (2007) 81–102
"... A challenge in hybrid evolutionary algorithms is to employ efficient strategies to cover all the search space, applying local search only in actually promising search areas. The inspiration in nature has been pursued to design flexible, coherent and efficient computational models. In this chapter, ..."
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Cited by 5 (1 self)
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A challenge in hybrid evolutionary algorithms is to employ efficient strategies to cover all the search space, applying local search only in actually promising search areas. The inspiration in nature has been pursued to design flexible, coherent and efficient computational models. In this chapter, the Clustering Search (*CS) is proposed as a generic way of combining search metaheuristics with clustering to detect promising search areas before applying local search procedures. The clustering process aims to gather similar information about the search space into groups, maintaining a representative solution associated to this information. Two applications are examined for combinatorial and continuous optimization problems, presenting how to develop hybrid evolutionary algorithms based on *CS.
Population training heuristics
 Lecture Notes in Computer Science
, 2005
"... Abstract. This work describes a new way of employing problemspecific heuristics to improve evolutionary algorithms: the Population Training Heuristic (PTH). The PTH employs heuristics in fitness definition, guiding the population to settle down in search areas where the individuals can not be impro ..."
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Cited by 4 (0 self)
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Abstract. This work describes a new way of employing problemspecific heuristics to improve evolutionary algorithms: the Population Training Heuristic (PTH). The PTH employs heuristics in fitness definition, guiding the population to settle down in search areas where the individuals can not be improved by such heuristics. Some new theoretical improvements not present in early algorithms are now introduced. An application for pattern sequencing problems is examined with new improved computational results. The method is also compared against other approaches, using benchmark instances taken from the literature. Keywords: Hybrid evolutionary algorithms; population training; MOSP; GMLP. 1