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11
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 238 (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.
Genetic Algorithm Difficulty and the Modality of Fitness Landscapes
 Foundations of Genetic Algorithms 3
, 1994
"... We assume that the modality (i.e., number of local optima) of a fitness landscape is related to the difficulty of finding the best point on that landscape by evolutionary computation (e.g., hillclimbers and genetic algorithms (GAs)). We first examine the limits of modality by constructing a unimodal ..."
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Cited by 56 (2 self)
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We assume that the modality (i.e., number of local optima) of a fitness landscape is related to the difficulty of finding the best point on that landscape by evolutionary computation (e.g., hillclimbers and genetic algorithms (GAs)). We first examine the limits of modality by constructing a unimodal function and a maximally multimodal function. At such extremes our intuition breaks down. A fitness landscape consisting entirely of a single hill leading to the global optimum proves to be hard for hillclimbers but apparently easy for GAs. A provably maximally multimodal function, in which half the points in the search space are local optima, can be easy for both hillclimbers and GAs. Exploring the more realistic intermediate range between the extremes of modality, we construct local optima with varying degrees of "attraction" to our evolutionary algorithms. Most work on optima and their basins of attraction has focused on hills and hillclimbers, while some research has explored attraction...
Evolutionary Monte Carlo: Applications to C_p Model Sampling and Change Point Problem
 STATISTICA SINICA
, 2000
"... Motivated by the success of genetic algorithms and simulated annealing in hard optimization problems, the authors propose a new Markov chain Monte Carlo (MCMC) algorithm so called an evolutionary Monte Carlo algorithm. This algorithm has incorporated several attractive features of genetic algorithms ..."
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Cited by 24 (5 self)
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Motivated by the success of genetic algorithms and simulated annealing in hard optimization problems, the authors propose a new Markov chain Monte Carlo (MCMC) algorithm so called an evolutionary Monte Carlo algorithm. This algorithm has incorporated several attractive features of genetic algorithms and simulated annealing into the framework of MCMC. It works by simulating a population of Markov chains in parallel, where each chain is attached to a different temperature. The population is updated by mutation (Metropolis update), crossover (partial state swapping) and exchange operators (full state swapping). The algorithm is illustrated through examples of the Cpbased model selection and changepoint identification. The numerical results and the extensive comparisons show that evolutionary Monte Carlo is a promising approach for simulation and optimization.
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.
An Analysis of a Reordering Operator with Tournament Selectionon a GAHard Problem
 Proceedings of Genetic and Evolutionary Computation Conference 2003 (GECCO2003
, 2003
"... This paper analyzes the performance of a genetic algorithm that utilizes tournament selection, onepoint crossover, and a reordering operator. A model is proposed to describe the combined effect of the reordering operator and tournament selection, and the numerical solutions are presented as well. P ..."
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Cited by 2 (2 self)
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This paper analyzes the performance of a genetic algorithm that utilizes tournament selection, onepoint crossover, and a reordering operator. A model is proposed to describe the combined effect of the reordering operator and tournament selection, and the numerical solutions are presented as well. Pairwise, sary, and probabilistic tournament selection are all included in the proposed model. It is also demonstrated that the upper bound of the probability to apply the reordering operator, previously derived with proportionate selection, does not affect the performance. Therefore, tournament selection is a necessity when using a reordering operator in a genetic algorithm.
Microscopic and Macroscopic Schema Theories for Genetic Programming and Variablelength Genetic Algorithms with OnePoint Crossover, their Use and their Relations with Earlier GP and GA Schema Theories
, 2000
"... A few schema theorems for GP have been proposed in the literature in the last few years. One of their main weaknesses is that they provide only a lower bound for the expected value of the number of instances of a given schema H at the next generation, E[m(H; t + 1)], rather than an exact value. Th ..."
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Cited by 1 (1 self)
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A few schema theorems for GP have been proposed in the literature in the last few years. One of their main weaknesses is that they provide only a lower bound for the expected value of the number of instances of a given schema H at the next generation, E[m(H; t + 1)], rather than an exact value. This paper presents new theoretical results for GP with onepoint crossover which overcome this problem. Firstly, we give an exact formulation (rather than a lower bound) for the expected number of instances of a schema at the next generation in terms of microscopic quantities. Thanks to this formulation we are then able to provide in improved version for an earlier GP schema theorem in which some (but not all) schema creation events are accounted for, thus obtaining a tighter bound for E[m(H; t + 1)]. Then, we extend the microscopic schema theorem to obtain an exact formulation of E[m(H; t + 1)] in terms of macroscopic quantities. In this formulation E[m(H; t + 1)] is a function of the...
Just What Are Building Blocks?
"... Abstract. Using an exact coarsegrained formulation of the dynamics of a GA we investigate in the context of a tunable family of “modular” fitness landscapes under what circumstances one would expect recombination to be “useful”. We show that this depends not only on the fitness landscape and the st ..."
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Abstract. Using an exact coarsegrained formulation of the dynamics of a GA we investigate in the context of a tunable family of “modular” fitness landscapes under what circumstances one would expect recombination to be “useful”. We show that this depends not only on the fitness landscape and the state of the population but also on the particular crossover mask under consideration. We conclude that rather than ask when recombination is useful or not one needs to ask what crossover masks are useful. We show that the answer to this is when the natural “building blocks ” of the landscape are compatible with the “building blocks ” defined by the crossover mask. 1
By
, 2001
"... A genetic algorithm is a biologically inspired method for function optimization that is loosely based on the theory of evolution. It is typically used when there is little knowledge of the solution space or when the search space is prohibitively large. Despite years of successful application to a va ..."
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A genetic algorithm is a biologically inspired method for function optimization that is loosely based on the theory of evolution. It is typically used when there is little knowledge of the solution space or when the search space is prohibitively large. Despite years of successful application to a variety of problems ranging from superstructure design to simulation of bacteria, little has been done to characterize the complexity of problems as they relate to this method. Complexity is critical however. Though the metaphor is seductive, the genetic algorithm is only a simulation of evolution. The accuracy of this simulation changes daily as more knowledge is gained from the biological communities and as computer researchers find better methods for solving problems regardless of the strict adherence to the biological inspiration. In the end, the genetic algorithm is a search method that must be implemented as a program consisting of loops and if statements. Given this, it seems reasonable to evaluate whether programs developed using this method provide any advantage in terms of efficiency over other methods. To do this, there must first be a
General Terms
"... We give a new interpretation to the concept of “landscape”. This allows us to develop a new theoretical model to study search algorithms. Particularly, we are able to quantify the amount and quality of “information ” embedded in a landscape and to predict the performance of a search algorithm over i ..."
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We give a new interpretation to the concept of “landscape”. This allows us to develop a new theoretical model to study search algorithms. Particularly, we are able to quantify the amount and quality of “information ” embedded in a landscape and to predict the performance of a search algorithm over it. We conclude presenting empirical results for a simple genetic algorithm which strongly support this idea.