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Substructural Neighborhoods for Local Search in the Bayesian Optimization Algorithm
, 2006
"... This paper studies the utility of using substructural neighborhoods for local search in the Bayesian optimization algorithm (BOA). The probabilistic model of BOA, which automatically identifies important problem substructures, is used to define the structure of the neighborhoods used in local search ..."
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Cited by 20 (14 self)
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This paper studies the utility of using substructural neighborhoods for local search in the Bayesian optimization algorithm (BOA). The probabilistic model of BOA, which automatically identifies important problem substructures, is used to define the structure of the neighborhoods used in local search. Additionally, a surrogate fitness model is considered to evaluate the improvement of the local search steps. The results show that performing substructural local search in BOA significatively reduces the number of generations necessary to converge to optimal solutions and thus provides substantial speedups.
Sporadic model building for efficiency enhancement of hierarchical BOA
, 2007
"... Efficiency enhancement techniques—such as parallelization and hybridization—are among the most important ingredients of practical applications of genetic and evolutionary algorithms and that is why this research area represents an important niche of evolutionary computation. This paper describes and ..."
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Cited by 17 (9 self)
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Efficiency enhancement techniques—such as parallelization and hybridization—are among the most important ingredients of practical applications of genetic and evolutionary algorithms and that is why this research area represents an important niche of evolutionary computation. This paper describes and analyzes sporadic model building, which can be used to enhance the efficiency of the hierarchical Bayesian optimization algorithm (hBOA) and other estimation of distribution algorithms (EDAs) that use complex multivariate probabilistic models. With sporadic model building, the structure of the probabilistic model is updated once in every few iterations (generations), whereas in the remaining iterations, only model parameters (conditional and marginal probabilities) are updated. Since the time complexity of updating model parameters is much lower than the time complexity of learning the model structure, sporadic model building decreases the overall time complexity of model building. The paper shows that for boundedly difficult nearly decomposable and hierarchical optimization problems, sporadic model building leads to a significant modelbuilding speedup, which decreases the asymptotic time complexity of model building in hBOA by a factor of Θ(n 0.26) to Θ(n 0.5), where n is the problem size. On the other hand, sporadic model building also increases the number of evaluations until convergence; nonetheless, if model building is the bottleneck, the evaluation slowdown is insignificant compared to the gains in the asymptotic complexity of model building. The paper also presents a dimensional model to provide a heuristic for scaling the structurebuilding period, which is the only parameter of the proposed sporadic modelbuilding approach. The paper then tests the proposed method and the rule for setting the structurebuilding period on the problem of finding ground states of 2D and 3D Ising spin glasses.
Evaluation relaxation using substructural information and linear estimation
 In Keijzer, M., et al. (Eds.), Proceedings of the ACM SIGEVO Genetic and Evolutionary Computation Conference (GECCO2006
, 2006
"... The paper presents an evaluationrelaxation scheme where a fitness surrogate automatically adapts to the problem structure and the partial contributions of subsolutions to the fitness of an individual are estimated efficiently and accurately. In particular, the probabilistic model built by extended ..."
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Cited by 11 (6 self)
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The paper presents an evaluationrelaxation scheme where a fitness surrogate automatically adapts to the problem structure and the partial contributions of subsolutions to the fitness of an individual are estimated efficiently and accurately. In particular, the probabilistic model built by extended compact genetic algorithm is used to infer the structural form of the surrogate and a least squares method is used to estimate the coefficients of the surrogate. Using the surrogate avoids the need for expensive fitness evaluation for some of the solutions, and thereby yields significant efficiency enhancement. Results show that a surrogate, which automatically adapts to problem knowledge mined from probabilistic models, yields substantial speedup (1.75–3.1) on a class of boundedlydifficult additivelydecomposable problems with and without additive Gaussian noise. The speedup provided by the surrogate increases with the number of substructures, substructure complexity, and noisetosignal ratio.
Influence of selection and replacement strategies on linkage learning in BOA
 In Proceedings of 2007 IEEE Congress on Evolutionary Computation (CEC 2007
, 2007
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Model Accuracy in the Bayesian Optimization Algorithm
, 2010
"... Evolutionary algorithms (EAs) are particularly suited to solve problems for which there is not much information available. From this standpoint, estimation of distribution algorithms (EDAs), which guide the search by using probabilistic models of the population, have brought a new view to evolutiona ..."
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Cited by 7 (4 self)
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Evolutionary algorithms (EAs) are particularly suited to solve problems for which there is not much information available. From this standpoint, estimation of distribution algorithms (EDAs), which guide the search by using probabilistic models of the population, have brought a new view to evolutionary computation. While solving a given problem with an EDA, the user has access to a set of models that reveal probabilistic dependencies between variables, an important source of information about the problem. However, as the complexity of the used models increases, the chance of overfitting and consequently reducing model interpretability, increases as well. This paper investigates the relationship between the probabilistic models learned by the Bayesian optimization algorithm (BOA) and the underlying problem structure. The purpose of the paper is threefold. First, model building in BOA is analyzed to understand how the problem structure is learned. Second, it is shown how the selection operator can lead to model overfitting in Bayesian EDAs. Third, the scoring metric that guides the search for an adequate model structure is modified to take into account the nonuniform distribution of the mating pool generated by tournament selection. Overall, this paper makes a contribution towards
D.: Overcoming hierarchical difficulty by hillclimbing the building block structure
 Genetic and Evolutionary Computation Conference (GECCO2007) (2007) 1256–1263
"... The Building Block Hypothesis suggests that Genetic Algorithms (GAs) are wellsuited for hierarchical problems, where efficient solving requires proper problem decomposition and assembly of solution from subsolution with strong nonlinear interdependencies. The paper proposes a hillclimber operatin ..."
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Cited by 6 (0 self)
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The Building Block Hypothesis suggests that Genetic Algorithms (GAs) are wellsuited for hierarchical problems, where efficient solving requires proper problem decomposition and assembly of solution from subsolution with strong nonlinear interdependencies. The paper proposes a hillclimber operating over the building block (BB) space that can efficiently address hierarchical problems. The new Building Block HillClimber (BBHC) uses hillclimb search experience to learn the problem structure. The neighborhood structure is adapted whenever new knowledge about the underlying BB structure is incorporated into the search. This allows the method to climb the hierarchical structure by revealing and solving consecutively the hierarchical levels. It is expected that for fully nondeceptive hierarchical BB structures the BBHC can solve hierarchical problems in linearithmic time. Empirical results confirm that the proposed method scales almost linearly with the problem size thus clearly outperforms population based recombinative methods.
Enhancing the Efficiency of The ECGA
 Proceedings of the X Parallel Problem Solving From Nature (PPSN2008
, 2008
"... Evolutionary Algorithms are largely used search and optimization procedures. They have been successfully applied for several problems and with proper care on the design process they can solve hard problems accurately, efficiently and reliably. The proper design of the algorithm turns some problems f ..."
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Cited by 5 (3 self)
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Evolutionary Algorithms are largely used search and optimization procedures. They have been successfully applied for several problems and with proper care on the design process they can solve hard problems accurately, efficiently and reliably. The proper design of the algorithm turns some problems from intractable to tractable. We can go even further, using efficiency enhancements to turn them from tractable to practical. In this paper we show preliminary results of two efficiency enhancements proposed for Extended Compact Genetic Algorithm. First, a model building enhancement was used to reduce the complexity of the process from O(n 3) to O(n 2), speeding up the algorithm by 1000 times on a 4096 bits problem. Then, a localsearch hybridization was used to reduce the population size by at least 32 times, reducing the memory and running time required by the algorithm. These results draw the first steps toward a competent and efficient Genetic Algorithm.
Sensibility of linkage information and effectiveness of estimated distributions. Evolutionary Computation
"... The probabilistic model building performed by estimation of distribution algorithms (EDAs) enables these methods to use advanced techniques of statistics and machine learning for automatic discovery of problem structures. However, in some situations, it may not be possible to completely and accurate ..."
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Cited by 1 (1 self)
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The probabilistic model building performed by estimation of distribution algorithms (EDAs) enables these methods to use advanced techniques of statistics and machine learning for automatic discovery of problem structures. However, in some situations, it may not be possible to completely and accurately identify the whole problem structure by probabilistic modeling due to certain inherent properties of the given problem. In this work, we illustrate one possible cause of such situations with problems consisting of structures with unequal fitness contributions. Based on the illustrative example, we introduce a notion that the estimated probabilisticmodels should be inspected to reveal the effective search directions and further propose a general approach which utilizes a reserved set of solutions to examine the built model for likely inaccurate fragments. Furthermore, the proposed approach is implemented on the extended compact genetic algorithm (ECGA) and experiments are performed on several sets of additively separable problems with different scaling setups. The results indicate that the proposed method can significantly assist ECGA to handle problems comprising structures of disparate fitness contributions and therefore may potentially help EDAs in general to overcome those situations in which the entire problem structure cannot be recognized properly due to the temporal delay of emergence of some promising partial solutions.
Genetic algorithm integrated with artificial chromosomes for multiobjective flowshop scheduling problems
"... a b s t r a c t Recently, a wealthy of research works has been dedicated to the design of effective and efficient genetic algorithms in dealing with multiobjective scheduling problems. In this paper, an artificial chromosome generating mechanism is designed to reserve patterns of genes in elite ch ..."
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a b s t r a c t Recently, a wealthy of research works has been dedicated to the design of effective and efficient genetic algorithms in dealing with multiobjective scheduling problems. In this paper, an artificial chromosome generating mechanism is designed to reserve patterns of genes in elite chromosomes and to find possible better solutions. The artificial chromosome generating mechanism is embedded in simple genetic algorithm (SGA) and the nondominated sorting genetic algorithm (NSGAII) to solve singleobjective and multiobjective flowshopscheduling problems, respectively. The singleobjective problems are to minimize the makespan while the multiobjective scheduling problems are to minimize the makespan and the maximum tardiness. Extensive numerical studies are conducted and the results indicate that artificial chromosomes embedded with SGA and NSGAII are able to further speed up the convergence of the genetic algorithm and improve the solution quality. This promising result may be of interests to industrial practitioners and academic researchers in the field of evolutionary algorithm or machine scheduling.
GAbased Path Planning for Mobile Robots: An Empirical Evaluation of Seven Techniques
"... Abstract—Previous research suggests that genetic algorithms (GAs) offer a promising solution to path planning for mobile robots. We examine six simple GAs used in prior studies, comparing them to a new node sequence approach that includes a twostep fitness function. Through a series of repeated tri ..."
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Abstract—Previous research suggests that genetic algorithms (GAs) offer a promising solution to path planning for mobile robots. We examine six simple GAs used in prior studies, comparing them to a new node sequence approach that includes a twostep fitness function. Through a series of repeated trials using a simple 16x16 grid, a 100x100 grid, a 600x600 Mars landscape, and a complex mazelike environment, we compare the chromosome structures and fitness functions of these seven methods. The results of our empirical testing indicate that the proposed dual goal approach, which uses a fixed length chromosome structure, outperformed both monotonic and other node sequence approaches, consistently finding a feasible path in even the most challenging of these environments. Index Terms—artificial intelligence, routing, genetic algorithms, robotics. I.