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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
An Overview of Evolutionary Computation
, 1993
"... Evolutionary computation uses computational models of evolutionary processes as key elements in the design and implementation of computerbased problem solving systems. In this paper we provide an overview of evolutionary computation, and describe several evolutionary algorithms that are current ..."
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Cited by 106 (5 self)
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Evolutionary computation uses computational models of evolutionary processes as key elements in the design and implementation of computerbased problem solving systems. In this paper we provide an overview of evolutionary computation, and describe several evolutionary algorithms that are currently of interest. Important similarities and differences are noted, which lead to a discussion of important issues that need to be resolved, and items for future research.
Crossover or Mutation?
 Foundations of Genetic Algorithms 2
, 1992
"... Genetic algorithms rely on two genetic operators  crossover and mutation. Although there exists a large body of conventional wisdom concerning the roles of crossover and mutation, these roles have not been captured in a theoretical fashion. For example, it has never been theoretically shown that mu ..."
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Cited by 70 (3 self)
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Genetic algorithms rely on two genetic operators  crossover and mutation. Although there exists a large body of conventional wisdom concerning the roles of crossover and mutation, these roles have not been captured in a theoretical fashion. For example, it has never been theoretically shown that mutation is in some sense "less powerful" than crossover or vice versa. This paper provides some answers to these questions by theoretically demonstrating that there are some important characteristics of each operator that are not captured by the other.
Genetic algorithms and scatter search: unsuspected potentials
 Statistics and Computing
, 1994
"... We provide a tutorial survey of connections between genetic algorithms and scatter search that have useful implications for developing new methods for optimization problems. The links between these approaches are rooted in principles underlying mathematical relaxations, which became inherited and ex ..."
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Cited by 25 (3 self)
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We provide a tutorial survey of connections between genetic algorithms and scatter search that have useful implications for developing new methods for optimization problems. The links between these approaches are rooted in principles underlying mathematical relaxations, which became inherited and extended by scatter search. Hybrid methods incorporating elements of genetic algorithms and scatter search are beginning to be explored in the literature, and we demonstrate that the opportunity exists to develop more advanced procedures that make fuller use of scatter search strategies and their recent extensions.
A new approach for solving nonlinear equations systems
 IEEE Transactions on Systems, Man and Cybernetics Part A
"... Abstract—This paper proposes a new perspective for solving systems of complex nonlinear equations by simply viewing them as a multiobjective optimization problem. Every equation in the system represents an objective function whose goal is to minimize the difference between the right and left terms o ..."
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Cited by 7 (2 self)
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Abstract—This paper proposes a new perspective for solving systems of complex nonlinear equations by simply viewing them as a multiobjective optimization problem. Every equation in the system represents an objective function whose goal is to minimize the difference between the right and left terms of the corresponding equation. An evolutionary computation technique is applied to solve the problem obtained by transforming the system into a multiobjective optimization problem. The results obtained are compared with a very new technique that is considered as efficient and is also compared with some of the standard techniques that are used for solving nonlinear equations systems. Several wellknown and difficult applications (such as interval arithmetic benchmark, kinematic application, neuropsychology application, combustion application, and chemical equilibrium application) are considered for testing the performance of the new approach. Empirical results reveal that the proposed approach is able to deal with highdimensional equations systems. Index Terms—Computational intelligence, evolutionary multiobjective optimization, metaheuristics, nonlinear equation systems. I.
Solving geometrical place problems by using Evolutionary Algorithms
 In Proceedings of World Computer Congress, M. Kaaniche (Ed
, 2004
"... Abstract Geometrical place can be sometimes difficult to find by applying mathematical methods. Evolutionary algorithms deal with a population of solutions. This population (initially random generated) is evolved using genetic operators. Population obtained after a specified number of iterations wil ..."
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Cited by 4 (3 self)
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Abstract Geometrical place can be sometimes difficult to find by applying mathematical methods. Evolutionary algorithms deal with a population of solutions. This population (initially random generated) is evolved using genetic operators. Population obtained after a specified number of iterations will contain solutions which (very likely) accomplish the geometrical place problem conditions. Geometrical place usually consists in more than one solution. By applying evolutionary algorithms to geometrical place problems multiple solutions can be obtained in a single run.
A New Crossover Technique for Cartesian Genetic Programming
, 2007
"... Genetic Programming was first introduced by Koza using tree representation together with a crossover technique in which random subbranches of the parents ’ trees are swapped to create the offspring. Later Miller and Thomson introduced Cartesian Genetic Programming, which uses directed graphs as a r ..."
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Cited by 3 (0 self)
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Genetic Programming was first introduced by Koza using tree representation together with a crossover technique in which random subbranches of the parents ’ trees are swapped to create the offspring. Later Miller and Thomson introduced Cartesian Genetic Programming, which uses directed graphs as a representation to replace the tree structures originally introduced by Koza. Cartesian Genetic Programming has been shown to perform better than the traditional Genetic Programming; but it does not use crossover to create offspring, it is implemented using mutation only. In this paper a new crossover method in Genetic Programming is introduced. The new technique is based on an adaptation of the Cartesian Genetic Programming representation and is tested on two simple regression problems. It is shown that by implementing the new crossover technique, convergence is faster than that of using mutation only in the Cartesian Genetic Programming method.
Exploration of Multiple Roots for a Polynomial System
"... Several problems from engineering, chemistry, medicine, etc. can be formulated as a system of equations. Finding a solution for such a system sometimes requires high computational efforts. There are situations when these systems have multiple solutions. For such problems, the task is to find as many ..."
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Cited by 1 (1 self)
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Several problems from engineering, chemistry, medicine, etc. can be formulated as a system of equations. Finding a solution for such a system sometimes requires high computational efforts. There are situations when these systems have multiple solutions. For such problems, the task is to find as many solutions as possible. In this paper, we deal with such systems of equations, which have multiple solutions and we attempt to solve them using two different approaches. Both approaches transform the problem into an optimization problem. The two approaches proposed in are (1) a modified line search and (2) an evolutionary algorithm. Several experiments are performed in order to emphasize the advantages and disadvantages of the two methods. 1.