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101
Hierarchical Bayesian Optimization Algorithm = Bayesian Optimization Algorithm + Niching + Local Structures
, 2001
"... The paper describes the hierarchical Bayesian optimization algorithm which combines the Bayesian optimization algorithm, local structures in Bayesian networks, and a powerful niching technique. The proposed algorithm is able to solve hierarchical traps and other difficult problems very efficiently. ..."
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Cited by 329 (70 self)
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The paper describes the hierarchical Bayesian optimization algorithm which combines the Bayesian optimization algorithm, local structures in Bayesian networks, and a powerful niching technique. The proposed algorithm is able to solve hierarchical traps and other difficult problems very efficiently.
Quantuminspired Evolutionary Algorithm for a Class of Combinatorial Optimization
 IEEE TRANS. EVOLUTIONARY COMPUTATION
, 2002
"... This paper proposes a novel evolutionary algorithm inspired by quantum computing, called a quantuminspired evolutionary algorithm (QEA), which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Like other evolutionary algorithms, QEA is a ..."
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Cited by 112 (7 self)
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This paper proposes a novel evolutionary algorithm inspired by quantum computing, called a quantuminspired evolutionary algorithm (QEA), which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Like other evolutionary algorithms, QEA is also characterized by the representation of the individual, the evaluation function, and the population dynamics. However, instead of binary, numeric, or symbolic representation, QEA uses a Qbit, defined as the smallest unit of information, for the probabilistic representation and a Qbit individual as a string of Qbits. A Qgate is introduced as a variation operator to drive the individuals toward better solutions. To demonstrate its effectiveness and applicability, experiments are carried out on the knapsack problem, which is a wellknown combinatorial optimization problem. The results show that QEA performs well, even with a small population, without premature convergence as compared to the conventional genetic algorithm.
Hierarchical BOA Solves Ising Spin Glasses and MAXSAT
 In Proc. of the Genetic and Evolutionary Computation Conference (GECCO 2003), number 2724 in LNCS
, 2003
"... Theoretical and empirical evidence exists that the hierarchical Bayesian optimization algorithm (hBOA) can solve challenging hierarchical problems and anything easier. This paper applies hBOA to two important classes of realworld problems: Ising spinglass systems and maximum satis ability (MAX ..."
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Cited by 56 (19 self)
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Theoretical and empirical evidence exists that the hierarchical Bayesian optimization algorithm (hBOA) can solve challenging hierarchical problems and anything easier. This paper applies hBOA to two important classes of realworld problems: Ising spinglass systems and maximum satis ability (MAXSAT). The paper shows how easy it is to apply hBOA to realworld optimization problems. The results indicate that hBOA is capable of solving enormously dicult problems that cannot be solved by other optimizers and still provide competitive or better performance than problemspeci c approaches on other problems. The results thus con rm that hBOA is a practical, robust, and scalable technique for solving challenging realworld problems.
Rulebased Evolutionary Online Learning Systems: LEARNING BOUNDS, CLASSIFICATION, AND PREDICTION
, 2004
"... Rulebased evolutionary online learning systems, often referred to as Michiganstyle learning classifier systems (LCSs), were proposed nearly thirty years ago (Holland, 1976; Holland, 1977) originally calling them cognitive systems. LCSs combine the strength of reinforcement learning with the genera ..."
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Cited by 54 (10 self)
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Rulebased evolutionary online learning systems, often referred to as Michiganstyle learning classifier systems (LCSs), were proposed nearly thirty years ago (Holland, 1976; Holland, 1977) originally calling them cognitive systems. LCSs combine the strength of reinforcement learning with the generalization capabilities of genetic algorithms promising a flexible, online generalizing, solely reinforcement dependent learning system. However, despite several initial successful applications of LCSs and their interesting relations with animal learning and cognition, understanding of the systems remained somewhat obscured. Questions concerning learning complexity or convergence remained unanswered. Performance in different problem types, problem structures, concept spaces, and hypothesis spaces stayed nearly unpredictable. This thesis has the following three major objectives: (1) to establish a facetwise theory approach for LCSs that promotes system analysis, understanding, and design; (2) to analyze, evaluate, and enhance the XCS classifier system (Wilson, 1995) by the means of the facetwise approach establishing a fundamental XCS learning theory; (3) to identify both the major advantages of an LCSbased learning approach as well as the most promising potential application areas. Achieving these three objectives leads to a rigorous understanding
Probabilistic Model Building and Competent Genetic Programming
 GENETIC PROGRAMMING THEORY AND PRACTISE, CHAPTER 13
, 2003
"... This paper describes a probabilistic model building genetic programming (PMBGP) developed based on the extended compact genetic algorithm (eCGA). Unlike traditional genetic programming, which use fixed recombination operators, the proposed PMBGA adapts linkages. The proposed algorithms... ..."
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Cited by 47 (10 self)
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This paper describes a probabilistic model building genetic programming (PMBGP) developed based on the extended compact genetic algorithm (eCGA). Unlike traditional genetic programming, which use fixed recombination operators, the proposed PMBGA adapts linkages. The proposed algorithms...
Fitness inheritance in the Bayesian optimization algorithm
, 2004
"... This paper describes how fitness inheritance can be used to estimate fitness for a proportion of newly sampled candidate solutions in the Bayesian optimization algorithm (BOA). The goal of estimating fitness for some candidate solutions is to reduce the number of fitness evaluations for problems whe ..."
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Cited by 33 (23 self)
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This paper describes how fitness inheritance can be used to estimate fitness for a proportion of newly sampled candidate solutions in the Bayesian optimization algorithm (BOA). The goal of estimating fitness for some candidate solutions is to reduce the number of fitness evaluations for problems where fitness evaluation is expensive. Bayesian networks used in BOA to model promising solutions and generate the new ones are extended to allow not only for modeling and sampling candidate solutions, but also for estimating their fitness. The results indicate that fitness inheritance is a promising concept in BOA, because populationsizing requirements for building appropriate models of promising solutions lead to good fitness estimates even if only a small proportion of candidate solutions is evaluated using the actual fitness function. This can lead to a reduction of the number of actual fitness evaluations by a factor of 30 or more.
Designing competent mutation operators via probabilistic model building of neighborhoods
 In Deb, K., & et al. (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO2004), Part II, LNCS 3103
, 2004
"... This paper presents a competent selectomutative genetic algorithm (GA), that adapts linkage and solves hard problems quickly, reliably, and accurately. A probabilistic model building process is used to automatically identify key building blocks (BBs) of the search problem. The mutation operator uses ..."
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Cited by 32 (20 self)
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This paper presents a competent selectomutative genetic algorithm (GA), that adapts linkage and solves hard problems quickly, reliably, and accurately. A probabilistic model building process is used to automatically identify key building blocks (BBs) of the search problem. The mutation operator uses the probabilistic model of linkage groups to find the best among competing building blocks. The competent selectomutative GA successfully solves additively separable problems of bounded difficulty, requiring only subquadratic number of function evaluations. The results show that for additively separable problems the probabilistic model building BBwise mutation scales as O(2 k m 1.5), and requires O ( √ k log m) less function evaluations than its selectorecombinative counterpart, confirming theoretical results reported elsewhere (Sastry & Goldberg, 2004). 1
Grammar Modelbased Program Evolution
 In Proceedings of the 2004 IEEE Congress on Evolutionary Computation
, 2004
"... In Evolutionary Computation, genetic operators, such as mutation and crossover, are employed to perturb individuals to generate the next population. However these fixed, problem independent genetic operators may destroy the subsolution, usually called building blocks, instead of discovering and pres ..."
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Cited by 28 (1 self)
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In Evolutionary Computation, genetic operators, such as mutation and crossover, are employed to perturb individuals to generate the next population. However these fixed, problem independent genetic operators may destroy the subsolution, usually called building blocks, instead of discovering and preserving them. One way to overcome this problem is to build a model based on the good individuals, and sample this model to obtain the next population. There is a wide range of such work in Genetic Algorithms
Parallel estimation of distribution algorithms
, 2002
"... The thesis deals with the new evolutionary paradigm based on the concept of Estimation of Distribution Algorithms (EDAs) that use probabilistic model of promising solutions found so far to obtain new candidate solutions of optimized problem. There are six primary goals of this thesis: 1. Suggestion ..."
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Cited by 26 (4 self)
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The thesis deals with the new evolutionary paradigm based on the concept of Estimation of Distribution Algorithms (EDAs) that use probabilistic model of promising solutions found so far to obtain new candidate solutions of optimized problem. There are six primary goals of this thesis: 1. Suggestion of a new formal description of EDA algorithm. This high level concept can be used to compare the generality of various probabilistic models by comparing the properties of underlying mappings. Also, some convergence issues are discussed and theoretical ways for further improvements are proposed. 2. Development of new probabilistic model and methods capable of dealing with continuous parameters. The resulting Mixed Bayesian Optimization Algorithm (MBOA) uses a set of decision trees to express the probability model. Its main advantage against the mostly used IDEA and EGNA approach is its backward compatibility with discrete domains, so it is uniquely capable of learning linkage between mixed continuousdiscrete genes. MBOA handles the discretization of continuous parameters as an integral part of the learning process, which outperforms the histogrambased
Realcoded bayesian optimization algorithm: Bringing the strength of BOA into the continuous world
 Proceedings of the Genetic and Evolutionary Computation Conference (GECCO2004
, 2004
"... Abstract. This paper describes a continuous estimation of distribution algorithm (EDA) to solve decomposable, realvalued optimization problems quickly, accurately, and reliably. This is the realcoded Bayesian optimization algorithm (rBOA). The objective is to bring the strength of (discrete) BOA ..."
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Cited by 24 (1 self)
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Abstract. This paper describes a continuous estimation of distribution algorithm (EDA) to solve decomposable, realvalued optimization problems quickly, accurately, and reliably. This is the realcoded Bayesian optimization algorithm (rBOA). The objective is to bring the strength of (discrete) BOA to bear upon the area of realvalued optimization. That is, the rBOA must properly decompose a problem, efficiently fit each subproblem, and effectively exploit the results so that correct linkage learning even on nonlinearity and probabilistic buildingblock crossover (PBBC) are performed for realvalued multivariate variables. The idea is to perform a Bayesian factorization of a mixture of probability distributions, find maximal connected subgraphs (i.e. substructures) of the Bayesian factorization graph (i.e., the structure of a probabilistic model), independently fit each substructure by a mixture distribution estimated from clustering results in the corresponding partialstring space (i.e., subspace, subproblem), and draw the offspring by an independent subspacebased sampling. Experimental results show that the rBOA finds, with a sublinear scaleup behavior for decomposable problems, a solution that is superior in quality to that found by a mixed iterative densityestimation evolutionary algorithm (mIDEA) as the problem size grows. Moreover, the rBOA generally outperforms the mIDEA on wellknown benchmarks for realvalued optimization. 1