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11
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 25 (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
Matching inductive search bias and problem structure in continuous estimation of distribution algorithms
 European Journal of Operational Research
"... Research into the dynamics of Genetic Algorithms (GAs) has led to the ¯eld of Estimation{of{Distribution Algorithms (EDAs). For discrete search spaces, EDAs have been developed that have obtained very promising results on a wide variety of problems. In this paper we investigate the conditions under ..."
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Cited by 16 (3 self)
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Research into the dynamics of Genetic Algorithms (GAs) has led to the ¯eld of Estimation{of{Distribution Algorithms (EDAs). For discrete search spaces, EDAs have been developed that have obtained very promising results on a wide variety of problems. In this paper we investigate the conditions under which the adaptation of this technique to continuous search spaces fails to perform optimization e±ciently. We show that without careful interpretation and adaptation of lessons learned from discrete EDAs, continuous EDAs will fail to perform e±cient optimization on even some of the simplest problems. We reconsider the most important lessons to be learned in the design of EDAs and subsequently show how we can use this knowledge to extend continuous EDAs that were obtained by straightforward adaptation from the discrete domain so as to obtain an improvement in performance. Experimental results are presented to illustrate this improvement and to additionally con¯rm experimentally that a proper adaptation of discrete EDAs to the continuous case indeed requires careful consideration. Key words: Estimation{of{distribution algorithms; Numerical optimization;
Experimental Study: Hypergraph Partitioning Based on the Simple and Advanced Genetic Algorithm BMDA and
 In Proceedings of the Fifth International Conference on Soft Computing
, 1999
"... Abstract: This paper is an experimental study on hypegraph partitioning using the simple genetic algorithm (GA) based on the schema theorem and the advanced algorithms based on the estimation of distribution of promising solution. Primarily we have implemented a simple GA based on the GaLib library[ ..."
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Cited by 11 (4 self)
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Abstract: This paper is an experimental study on hypegraph partitioning using the simple genetic algorithm (GA) based on the schema theorem and the advanced algorithms based on the estimation of distribution of promising solution. Primarily we have implemented a simple GA based on the GaLib library[Gal94] and some hybrid variant included a fast heuristics to speed up the convergence of the optimization process. Secondly we have implemented the Univariate Marginal Distribution algorithm (UMDA) and the Bivariate Marginal Distribution algorithm (BMDA), both have been published even recently[Pel98] and used a share version of a superior new program BOA based on the Bayesian Optimization Algorithm [Pel99]. We have also extended the BMDA algorithm to a new version with finite alphabet encoding of chromozomes and new metric that enables the mway partitioning graphs. The aim of our paper is to test the efficiency of new approaches for discrete combinatorial problems represented by hypergraph partitioning. Key words: decomposition, hypergraph partitioning, simple and hybrid GA, estimation of distribution algorithm, Bayesian network. 1
Automatic discovery of ranking formulas for playing with multiarmed
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iECGA: Integer extended compact genetic algorithm
 IN PROCEEDINGS OF ACM SIGEVO GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO2006
, 2006
"... Extended compact genetic algorithm (ECGA) is an algorithm that can solve hard problems in the binary domain. ECGA is reliable and accurate because of the capability of detecting building blocks, but certain difficulties are encountered when we directly apply ECGA to problems in the integer domain. I ..."
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Cited by 4 (1 self)
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Extended compact genetic algorithm (ECGA) is an algorithm that can solve hard problems in the binary domain. ECGA is reliable and accurate because of the capability of detecting building blocks, but certain difficulties are encountered when we directly apply ECGA to problems in the integer domain. In this paper, we propose a new algorithm that extends ECGA, called integer extended compact genetic algorithm (iECGA). iECGA uses a modified probability model and inherits the capability of detecting building blocks from ECGA. iECGA is specifically designed for problems in the integer domain and can avoid the difficulties that ECGA encounters. In the experimental results, we show the performance comparisons between ECGA, iECGA, and a simple GA, and the results indicate that iECGA has good performances on problems in the integer domain.
Multiobjective Bayesian Optimization Algorithm for Combinatorial Problems: Theory and practice
"... This paper deals with the utilizing of the Bayesian optimization algorithm (BOA) for the multiobjective optimization of combinatorial problems. Three probabilistic models used in the Estimation Distribution Algorithms (EDA), such as UMDA, BMDA and BOA which allow to search effectively on the promi ..."
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Cited by 3 (1 self)
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This paper deals with the utilizing of the Bayesian optimization algorithm (BOA) for the multiobjective optimization of combinatorial problems. Three probabilistic models used in the Estimation Distribution Algorithms (EDA), such as UMDA, BMDA and BOA which allow to search effectively on the promising areas of the combinatorial search space are discussed. The main attention is focused on the incorporation of Pareto optimality concept into classical structure of the BOA algorithm. We have modified the standard algorithm BOA for one criterion optimization utilizing the known niching techniques to find the Pareto optimal set. The experiments are focused on tree classes of the combinatorial problems: artificial problem with known Pareto set, multiple 0/1 knapsack problem and the bisectioning of hypergraphs as well.
Optimized lookahead tree search policies
"... Abstract. We consider in this paper lookahead tree techniques for the discretetime control of a deterministic dynamical system so as to maximize a sum of discounted rewards over an infinite time horizon. Given the current system state xt at time t, these techniques explore the lookahead tree repre ..."
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Abstract. We consider in this paper lookahead tree techniques for the discretetime control of a deterministic dynamical system so as to maximize a sum of discounted rewards over an infinite time horizon. Given the current system state xt at time t, these techniques explore the lookahead tree representing possible evolutions of the system states and rewards conditioned on subsequent actions ut, ut+1,.... When the computing budget is exhausted, they output the action ut that led to the best found sequence of discounted rewards. In this context, we are interested in computing good strategies for exploring the lookahead tree. We propose a generic approach that looks for such strategies by solving an optimization problem whose objective is to compute a (budget compliant) treeexploration strategy yielding a control policy maximizing the average return over a postulated set of initial states. This generic approach is fully specified to the case where the space of candidate treeexploration strategies are “bestfirst ” strategies parameterized by a linear combination of lookahead path features – some of them having been advocated in the literature before – and where the optimization problem is solved by using an EDAalgorithm based on Gaussian distributions. Numerical experiments carried out on a model of the treatment of the HIV infection show that the optimized treeexploration strategy is orders of magnitudes better than the previously advocated ones.
Evoptool: an Extensible Toolkit for Evolutionary Optimization Algorithms Comparison
"... Abstract — This paper presents Evolutionary Optimization Tool (Evoptool), an optimization toolkit that implements a set of metaheuristics based on the Evolutionary Computation paradigm. Evoptool provides a common platform for the development and test of new algorithms, in order to facilitate the pe ..."
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Abstract — This paper presents Evolutionary Optimization Tool (Evoptool), an optimization toolkit that implements a set of metaheuristics based on the Evolutionary Computation paradigm. Evoptool provides a common platform for the development and test of new algorithms, in order to facilitate the performance comparison activity. The toolkit offers a wide set of benchmark problems, from classical toy examples to complex tasks, and a collection of implementations of algorithms from the Genetic Algorithms and Estimation of Distribution Algorithms paradigms. Evoptool is flexible and easy to extend, also with algorithms based on other approaches that go beyond Evolutionary Computation. I.
Summary
, 2006
"... Heuristic search methods have been applied to a wide variety of optimisation problems. A central element of these algorithms ’ success is the correct choice of values for their control parameters. To tune these settings, the use of specialists ’ knowledge and experience are often required. In this t ..."
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Heuristic search methods have been applied to a wide variety of optimisation problems. A central element of these algorithms ’ success is the correct choice of values for their control parameters. To tune these settings, the use of specialists ’ knowledge and experience are often required. In this thesis, we first formalise the problem of parameter adaptation in heuristic search. Thereafter, we propose an automated mechanism, i.e. a method that reduces the strong dependency on experts, for choosing the best performing algorithm among several heuristic search approaches and optimising its parameters. The novel Multiple Algorithms ’ Parameter Adaptation Algorithm (MAPAA) is based on PopulationBased Incremental Learning, a method that combines the concepts of Competitive Learning and Genetic Algorithms. Addressing specific characteristics of the adaptation scenario, we extend the basic approach to deal with more versatile search spaces and to run successfully for small populations and few generational cycles. All newly introduced techniques are analysed in detail, and the efficiency and robustness of the MAPAA is studied and proven in several applications. I Acknowledgements I would like to express my deepest gratitude to my supervisor Professor Edward Tsang. He has been a remarkable mentor. Only his guidance, encouragement and seemingly infinite patience have made my research work possible.
Designing Genetic Algorithm Based on Exploration and Exploitation of Gene Linkage BY
"... ii Genetic algorithm (GA) is expected to realize black box optimization, which can solve optimization problems based only on the values of objective functions. Efficient building block mixing is essential in genetic algorithms. For simple GAs, it is not an easy task without prior knowledge of a prob ..."
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ii Genetic algorithm (GA) is expected to realize black box optimization, which can solve optimization problems based only on the values of objective functions. Efficient building block mixing is essential in genetic algorithms. For simple GAs, it is not an easy task without prior knowledge of a problem and such knowledge is not always available. GAs which can learn or detect problem structure automatically are called competent genetic algorithms (cGAs). This dissertation proposes two important parts to realize cGAs, (1) a novel approach to identify linkages and (2) a crossover for functions with complexly overlapping building blocks. First, we propose a novel linkage identification method called Dependency Detection for Distribution Derived from fitness Differences (D5), which detects linkage by estimating strings clustered according to fitness differences caused by perturbations. It is important to detect linkage — interaction between variables tightly linked to form a building block — to process building blocks effectively. The D5 inherits the merits of two classes of cGAs, estimation of distribution algorithms (EDAs) and perturbation methods (PMs), that is, it can detect linkages for problems which are difficult for EDAs requiring smaller computational cost than PMs. In addition, ContextDependent Crossover (CDC) has been developed to combine complexly overlapping building blocks. The CDC examine contexts of each pair of strings in addition to the linkage information to process building blocks. Combining the linkage identification and the crossover methods, we have realized a competent genetic algorithm applicable to widerspectrum realworld problems. iii