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79
Tabu Search
 Part I,” ORSA Journal on Computing
, 1989
"... Faced with the challenge of solving hard optimization problems that abound in the real world, classical methods often encounter great difficulty. Vitally important applications in business, engineering, economics and science cannot be tackled with any reasonable hope of success, within practical tim ..."
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Cited by 591 (38 self)
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Faced with the challenge of solving hard optimization problems that abound in the real world, classical methods often encounter great difficulty. Vitally important applications in business, engineering, economics and science cannot be tackled with any reasonable hope of success, within practical time horizons, by solution methods that have been the predominant focus of academic research throughout the past three decades (and which are still the focus of many textbooks). The metaheuristic approach called tabu search (TS) is dramatically changing our ability to solve problems of practical significance. Current applications of TS span the realms of resource planning, telecommunications, VLSI design, financial analysis, scheduling, space planning, energy distribution, molecular engineering, logistics, pattern classification, flexible manufacturing, waste management, mineral exploration, biomedical analysis, environmental conservation and scores of others. In recent years, journals in a wide variety of fields have published tutorial articles and computational studies documenting successes by tabu search in extending the frontier of problems that can be handled effectively — yielding solutions whose quality often significantly surpasses that obtained by methods previously applied. Table 1.1 gives a partial catalog of example applications. A more comprehensive list, including summary descriptions of gains achieved from practical implementations, can be found in Glover and Laguna, 1997. Recent TS developments and applications can also be found in the Tabu Search Vignettes section of the web page
From recombination of genes to the estimation of distributions I. binary parameters
, 1996
"... The Breeder Genetic Algorithm (BGA) is based on the equation for the response to selection. In order to use this equation for prediction, the variance of the fitness of the population has to be estimated. For the usual sexual recombination the computation can be difficult. In this paper we shortly ..."
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Cited by 236 (9 self)
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The Breeder Genetic Algorithm (BGA) is based on the equation for the response to selection. In order to use this equation for prediction, the variance of the fitness of the population has to be estimated. For the usual sexual recombination the computation can be difficult. In this paper we shortly state the problem and investigate several modifications of sexual recombination. The first method is gene pool recombination, which leads to marginal distribution algorithms. In the last part of the paper we discuss more sophisticated methods, based on estimating the distribution of promising points.
Designing Efficient And Accurate Parallel Genetic Algorithms
, 1999
"... Parallel implementations of genetic algorithms (GAs) are common, and, in most cases, they succeed to reduce the time required to find acceptable solutions. However, the effect of the parameters of parallel GAs on the quality of their search and on their efficiency are not well understood. This insuf ..."
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Cited by 222 (5 self)
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Parallel implementations of genetic algorithms (GAs) are common, and, in most cases, they succeed to reduce the time required to find acceptable solutions. However, the effect of the parameters of parallel GAs on the quality of their search and on their efficiency are not well understood. This insufficient knowledge limits our ability to design fast and accurate parallel GAs that reach the desired solutions in the shortest time possible. The goal of this dissertation is to advance the understanding of parallel GAs and to provide rational guidelines for their design. The research reported here considered three major types of parallel GAs: simple masterslave algorithms with one population, more sophisticated algorithms with multiple populations, and a hierarchical combination of the first two types. The investigation formulated simple models that predict accurately the quality of the solutions with different parameter settings. The quality predictors were transformed into populationsizing equations, which in turn were used to estimate the execution time of the algorithms.
The Equation for the Response to Selection and Its Use for Prediction
, 1997
"... The Breeder Genetic Algorithm (BGA) was designed according to the theories and methods used in the science of livestock breeding. The prediction of a breeding experiment is based on the response to selection (RS) equation. This equation relates the change in a population 's fitness to the standard d ..."
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Cited by 103 (15 self)
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The Breeder Genetic Algorithm (BGA) was designed according to the theories and methods used in the science of livestock breeding. The prediction of a breeding experiment is based on the response to selection (RS) equation. This equation relates the change in a population 's fitness to the standard deviation of its fitness, as well as to the parameters selection intensity and realized heritability. In this paper the exact RS equation is derived for proportionate selection given an infinite population in linkage equilibrium. In linkage equilibrium the genotype frequencies are the product of the univariate marginal frequencies. The equation contains Fisher's fundamental theorem of natural selection as an approximation. The theorem shows that the response is approximately equal to the quotient of a quantity called additive genetic variance, VA , and the average fitness. We compare Mendelian twoparent recombination with genepool recombination, which belongs to a special class of genetic ...
Schemata, Distributions and Graphical Models in Evolutionary Optimization
 Journal of Heuristics
, 1999
"... In this paper the optimization of additively decomposed discrete functions is investigated. For these functions genetic algorithms have exhibited a poor performance. First the schema theory of genetic algorithms is reformulated in probability theory terms. A schema denes the structure of a marginal ..."
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Cited by 88 (8 self)
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In this paper the optimization of additively decomposed discrete functions is investigated. For these functions genetic algorithms have exhibited a poor performance. First the schema theory of genetic algorithms is reformulated in probability theory terms. A schema denes the structure of a marginal distribution. Then the conceptual algorithm BEDA is introduced. BEDA uses a Boltzmann distribution to generate search points. From BEDA a new algorithm, FDA, is derived. FDA uses a factorization of the distribution. The factorization captures the structure of the given function. The factorization problem is closely connected to the theory of conditional independence graphs. For the test functions considered, the performance of FDA in number of generations till convergence is similar to that of a genetic algorithm for the OneMax function. This result is theoretically explained.
Reevaluating Genetic Algorithm Performance under Coordinate Rotation of Benchmark Functions  A survey of some theoretical and practical aspects of genetic algorithms
 BioSystems
, 1995
"... This work analyzes some concepts of genetic algorithms and explains why they may be applied with success to some problems in function optimization. In addition to other performance properties, it has been shown that genetic algorithms are able to overcome local minima in highly multimodal functions ..."
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Cited by 79 (17 self)
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This work analyzes some concepts of genetic algorithms and explains why they may be applied with success to some problems in function optimization. In addition to other performance properties, it has been shown that genetic algorithms are able to overcome local minima in highly multimodal functions (e.g., Rastrigin, Schwefel). The performance of genetic algorithms is supported by an extensive theory, which is based on the assumption of additive gene effects. But the current work shows that the assumption of additive gene effects is not weak, and that the dependence on specific parameter settings is much stronger than often believed. Furthermore, the assumptions regarding the fitness function are so restricting that slight modifications of the standard test functions cause a failure of the optimization procedure even though the function's structure is preserved. The current experiments focus on a few widelyused scalable test functions. the results indicate that a standard g...
FDA  A scalable evolutionary algorithm for the optimization of additively decomposed functions
, 1999
"... FDA  the Factorized Distribution Algorithm  is an evolutionary algorithm which combines mutation and recombination by using a distribution instead. The distribution is estimated from a set of selected points. In general a discrete distribution defined for n binary variables has 2 n parameters. T ..."
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Cited by 62 (7 self)
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FDA  the Factorized Distribution Algorithm  is an evolutionary algorithm which combines mutation and recombination by using a distribution instead. The distribution is estimated from a set of selected points. In general a discrete distribution defined for n binary variables has 2 n parameters. Therefore it is too expensive to compute. For additively decomposed discrete functions (ADFs) there exist algorithms which factor the distribution into conditional and marginal distributions. This factorization is used by FDA. The scaling of FDA is investigated theoretically and numerically. The scaling depends on the ADF structure and the specific assignment of function values. Difficult functions on a chain or a tree structure are solved in about O(n p n) operations. More standard genetic algorithms are not able to optimize these functions. FDA is not restricted to exact factorizations. It also works for approximate factorizations as is shown for a circle and a grid structure. By using results from Bayes networks, FDA is extended to LFDA. LFDA computes an approximate factorization using only the data, not the ADF structure. The scaling of LFDA is compared to the scaling of FDA. Keywords Genetic algorithms, Boltzmann distribution, simulated annealing, Bayes network, learning of Bayes networks, convergence, factorization of distributions. 1
Evaluationrelaxation schemes for genetic and evolutionary algorithms
, 2002
"... Genetic and evolutionary algorithms have been increasingly applied to solve complex, large scale search problems with mixed success. Competent genetic algorithms have been proposed to solve hard problems quickly, reliably and accurately. They have rendered problems that were difficult to solve by th ..."
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Cited by 60 (28 self)
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Genetic and evolutionary algorithms have been increasingly applied to solve complex, large scale search problems with mixed success. Competent genetic algorithms have been proposed to solve hard problems quickly, reliably and accurately. They have rendered problems that were difficult to solve by the earlier GAs to be solvable, requiring only a subquadratic number of function evaluations. To facilitate solving largescale complex problems, and to further enhance the performance of competent GAs, various efficiencyenhancement techniques have been developed. This study investigates one such class of efficiencyenhancement technique called evaluation relaxation. Evaluationrelaxation schemes replace a highcost, lowerror fitness function with a lowcost, higherror fitness function. The error in fitness functions comes in two flavors: Bias and variance. The presence of bias and variance in fitness functions is considered in isolation and strategies for increasing efficiency in both cases are developed. Specifically, approaches for choosing between two fitness functions with either differing variance or differing bias values have been developed. This thesis also investigates fitness inheritance as an evaluation
Finite Markov Chain Results in Evolutionary Computation: A Tour d'Horizon
, 1998
"... . The theory of evolutionary computation has been enhanced rapidly during the last decade. This survey is the attempt to summarize the results regarding the limit and finite time behavior of evolutionary algorithms with finite search spaces and discrete time scale. Results on evolutionary algorithms ..."
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Cited by 57 (2 self)
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. The theory of evolutionary computation has been enhanced rapidly during the last decade. This survey is the attempt to summarize the results regarding the limit and finite time behavior of evolutionary algorithms with finite search spaces and discrete time scale. Results on evolutionary algorithms beyond finite space and discrete time are also presented but with reduced elaboration. Keywords: evolutionary algorithms, limit behavior, finite time behavior 1. Introduction The field of evolutionary computation is mainly engaged in the development of optimization algorithms which design is inspired by principles of natural evolution. In most cases, the optimization task is of the following type: Find an element x 2 X such that f(x ) f(x) for all x 2 X , where f : X ! IR is the objective function to be maximized and X the search set. In the terminology of evolutionary computation, an individual is represented by an element of the Cartesian product X \Theta A, where A is a possibly...
Strategy Adaptation by Competing Subpopulations
 Parallel Problem Solving from Nature (PPSN III
, 1994
"... . The breeder genetic algorithm BGA depends on a set of control parameters and genetic operators. In this paper it is shown that strategy adaptation by competing subpopulations makes the BGA more robust and more efficient. Each subpopulation uses a different strategy which competes with other subpop ..."
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Cited by 47 (2 self)
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. The breeder genetic algorithm BGA depends on a set of control parameters and genetic operators. In this paper it is shown that strategy adaptation by competing subpopulations makes the BGA more robust and more efficient. Each subpopulation uses a different strategy which competes with other subpopulations. Numerical results are presented for a number of test functions. Keywords: breeder genetic algorithm, strategy adaptation, competition, multiresolution search 1 Introduction Many evolutionary algorithms depend on a set of control parameters. Often the optimal setting of the parameter depends on the particular application. Moreover the optimal control parameters may be different at the start of the run and at the end where the individuals are very similar to each other. Basically two approaches have been pursuit to solve the above problem. In the first approach some externally specified schedule is used. The schedule may depend for instance on the time, measured in number of genera...