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A Generalized Differential Evolution Combined with EDA for Multi-objective Optimization Problems

4th International Conference on Intelligent Computing

作者： Chen, Wang Shi, Yan-jun Teng, Hong-fei Dalian Univ Technol Sch Mech Engn Dalian 116024 Peoples R China

ISBN: (纸本)9783540859833

This paper proposed a multi-objective evolutionary algorithm (called by GDE-EDA hereinafter). The proposed algorithm combined a generalized differential evolution (DE) with an estimation of distribution algorithm (EDA). This combination can simultaneously use global information of population extracted by EDA and differential information by DE. Thus, GDE-EDA can obtain a better distribution of the solutions by EDA while keeping the fast convergence exhibited by DE. The experimental results of the proposed GDE-EDA algorithm were reported on a suit of widely used test functions, and compared with GDE and NSGA-II in the literature.

关键词： Generalized differential evolution estimation of distribution algorithm multi-objective optimization

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The role of a priori information in the minimization of contact potentials by means of estimation of distribution algorithms

The role of a priori information in the minimization of cont...

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5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBIO 2007)

作者： Santana, Roberto Larranaga, Pedro Lozano, Jose A. Univ Basque Country Dept Comp Sci & Artificial Intelligence Donostia San Sebastian Spain

ISBN: (纸本)9783540717829

Directed search methods and probabilistic approaches have been used as two alternative ways for computational protein design. This paper presents a hybrid methodology that combines features from both approaches. Three estimation of distribution algorithms are applied to the solution of a protein design problem by minimization of contact potentials. The combination of probabilistic models able to represent probabilistic dependencies with the use of information about residues interactions in the protein contact graph is shown to improve the efficiency of search for the problems evaluated.

关键词： estimation of distribution algorithm protein design energy minimization algorithms

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Dependency Trees, Permutations, and Quadratic Assignment Problem 07

Dependency Trees, Permutations, and Quadratic Assignment Pro...

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Annual Conference of Genetic and Evolutionary Computation Conference

作者： Pelikan, Martin Tsutsui, Shigeyoshi Kalapala, Rajiv Univ Missouri Dept Math & Comp Sci St Louis MO 63121 USA Hannan Univ Dept Management & Informat Sci Matsubara Osaka Japan

ISBN: (纸本)9781595936974

This paper describes and analyzes an estimation of distribution algorithm based on dependency tree models (dtEDA), which can explicitly encode probabilistic models for permutations. dtEDA is tested on deceptive ordering problems and a number of instances of the quadratic assignment problem. The performance of dtEDA is compared to that of the standard genetic algorithm with the partially matched crossover (PMX) and the linear order crossover (LOX). In the quadratic assignment problem, the robust tabu search is also included in the comparison.

关键词： estimation of distribution algorithm dependency tree permutation domain quadratic assignment problem deceptive ordering problem linkage learning

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A general-purpose tunable landscape generator

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2006年第5期10卷 590-603页

作者： Gallagher, Marcus Yuan, Bo Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld 4072 Australia

The research literature on metalieuristic and evolutionary computation has proposed a large number of algorithms for the solution of challenging real-world optimization problems. It is often not possible to study theoretically the performance of these algorithms unless significant assumptions are made on either the algorithm itself or the problems to which it is applied, or both. As a consequence, metalieuristics are typically evaluated empirically using a set of test problems. Unfortunately, relatively little attention has been given to the development of methodologies and tools for the large-scale empirical evaluation and/or comparison of metaheuristics. In this paper, we propose a landscape (test-problem) generator that can be used to generate optimization problem instances for continuous, bound-constrained optimization problems. The landscape generator is parameterized by a small number of parameters, and the values of these parameters have a direct and intuitive interpretation in terms of the geometric features of the landscapes that they produce. An experimental space is defined over algorithms and problems, via a tuple of parameters for any specified algorithm and problem class (here determined by the landscape generator). An experiment is then clearly specified as a point in this space, in a way that is analogous to other areas of experimental algorithmics, and more generally in experimental design. Experimental results are presented, demonstrating the use of the landscape generator. In particular, we analyze some simple, continuous estimation of distribution algorithms, and gain new insights into the behavior of these algorithms using the landscape generator.

关键词： continuous optimization empirical algorithm analysis estimation of distribution algorithm test-problem generator

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A new algorithm of automatic programming: GEGEP

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6th International Conference on Simulated Evolution and Learning

作者： Du, Xin Li, Yueqiao Xie, Datong Kang, Lishan China Univ Geosci Dept Comp Sci & Technol Wuhan Peoples R China Shijiazhuang Univ Econ Dept Informat & Engn Shijiazhuang Peoples R China Wuhan Univ State Key Lab Software Engn Wuhan 430072 Peoples R China

ISBN: (纸本)3540473319

Gene Expression Programming (GEP) has wide searching ability, simple representation, powerful genetic operators and the creation of high levels of complexity. However, it has some shortcomings, such as blind searching and when dealing with complex problems, its genotype under Karva notation does not allow hierarchical composition of the solution, which impairs the efficiency of the algorithm. So a new automatic programming method is proposed: Gene Estimated Gene Expression Programming(GEGEP) which combines the advantages of estimation of distribution algorithm (EDA) and basic GEP. Compared with basic GEP, it mainly has the following characteristics: First, improve the gene expression structure, the head of gene is divided into a head and a body, which can be used to introduce learning mechanism. Second, the homeotic gene which is also composed of a head, a body and a tail is used which can increase its searching ability. Third, the idea of EDA is introduced, which can enhance its learning ability and accelerate convergence rate. The results of experiments show that GEGEP has better fitting and predicted precision, faster convergence speed than basic GEP and traditional GP.

关键词： Gene Expression Programming genetic programming Gene Estimated Gene Expression Programming estimation of distribution algorithm

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Clustering and learning Gaussian distribution for continuous optimization

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS 2005年第2期35卷 195-204页

作者： Lu, Q Yao, X Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England

Since the estimation of distribution algorithm (EDA) was introduced, different approaches in continuous domains have been developed. Initially, the single Gaussian distribution was broadly used when building the probabilistic models, which would normally mislead the search when dealing with multimodal functions. Some researchers later constructed EDAs that take advantage of mixture probability distributions by using clustering techniques. But their algorithms all need prior knowledge before applying clustering, which is unreasonable in real life. In this paper, two new EDAs for continuous optimization are proposed, both of which, incorporate clustering techniques into estimation process to break the single Gaussian distribution assumption.. The new algorithms, Clustering and estimation of Gaussian Network algorithm based on BGe metric and Clustering and estimation of Gaussian distribution algorithm, not only show great advantage in optimizing multimodal functions with a few local optima, but also overcome the restriction of demanding prior knowledge before clustering by using a very reliable clustering technique, Rival Penalized Competitive Learning. This is the first time that EDAs have the ability to detect the number of global optima automatically. A set of experiments have been implemented to evaluate the performance of new algorithms. Besides the improvement over some multimodal functions, according to the No Free Lunch theory, their weak side is also showed.

关键词： clustering estimation of distribution algorithm Gaussian distribution

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DE/EDA: A new evolutionary algorithm for global optimization

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INFORMATION SCIENCES 2005年第3-4期169卷 249-262页

作者： Sun, JY Zhang, QF Tsang, EPK Univ Essex Dept Comp Sci Colchester CO4 3SQ Essex England

Differential evolution (DE) was very successful in solving the global continuous optimization problem. It mainly uses the distance and direction information from the current population to guide its further search. estimation of distribution algorithm (EDA) samples new solutions from a probability model which characterizes the distribution of promising solutions. This paper proposes a combination of DE and EDA (DE/EDA) for the global continuous optimization problem. DE/EDA combines global information extracted by EDA with differential information obtained by DE to create promising solutions. DE/EDA has been compared with the best version of the DE algorithm and an EDA on several commonly utilized test problems. Experimental results demonstrate that DE/EDA outperforms the DE algorithm and the EDA. The effect of the parameters of DE/EDA to its performance is investigated experimentally. (C) 2004 Elsevier Inc. All rights reserved.

关键词： differential evolution estimation of distribution algorithm global continuous optimization problem

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Probabilistic distribution models for EDA-based GP 05

Probabilistic distribution models for EDA-based GP

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Genetic and Evolutionary Computation Conference

作者： Yanai, Kohsuke Iba, Hitoshi Univ Tokyo Grad Sch Frontier Sci Dept Frontier Informat Kashiwa Chiba 2778561 Japan

ISBN: (纸本)1595930108

This paper proposes a novel technique for a program evolution based on probabilistic models. In the proposed method, two probabilistic distribution models with probabilistic dependencies between variables are used together. We empirically comfirm that our proposed method has higher search performance. Thereafter, we discuss the effectiveness of its distribution models.

关键词： algorithms genetic programming program evolution evolutionary computing estimation of distribution algorithm probabilistic model-building genetic algorithm

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Linkage identification for real-valued loci by fitness difference classification

Linkage identification for real-valued loci by fitness diffe...

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IEEE Congress on Evolutionary Computation

作者： Tsuji, M Munetomo, M Akama, K Hokkaido Univ Sapporo Hokkaido Japan

ISBN: (纸本)0780393635

In order to enhance efficiency of genetic algorithms, it is important to identify a linkage set, i.e. a set of loci tightly linked to construct a building block. In this paper, we propose a novel linkage identification method for real-valued strings called the real-valued Dependency Detection for distribution Derived from df (rD(5)). It can detect linkage sets with quasi-linear fitness evaluations. The rD(5) is designed based on the D-5 which has been proposed for binary strings. It detects dependencies of loci by estimating the distribution of strings classified according to fitness differences. The rD(5) and the LINC-R which is one of linkage identification methods proposed elsewhere, provide approximate equivalent information about a function to be solved, however, the rD(5) performs smaller number of fitness evaluations than the LINC-R for larger functions. Although estimation of distribution algorithms (EDAs) also estimate distribution of strings, it is difficult for EDAs to solve a function composed of exponentially scaled sub-functions. The proposed method, by contrast, can be applied to the function in the similar way to as to a function composed of uniformly scaled sub-functions which is easy for EDAs. We perform experiments to compare the proposed method with the LINC-R and to examine the scaling effect stability of the rD(5). We also investigate two parameters, that define the amount of perturbation (mutation) and that define the quantization level.

关键词： estimation theory genetic algorithms pattern classification string matching binary strings estimation of distribution algorithm exponentially scaled subfunctions fitness difference classification linkage identification mutation perturb Genetic algorithm (GA) binary string estimation theory String matching Pattern recognition Cosmic strings quantization level Allocation algorithm health

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Discovering gene-gene interactions by inferring dags of Bayesian networks with EDA

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11th Joint International Computer Conference (JICC 2005)

作者： Dai, C Liu, J Wuhan Univ Sch Comp Wuhan 430079 Peoples R China

ISBN: (纸本)9812565329

More and more experimental results have shown that genes do not function independently;instead they act on each other. To find the interactions among genes is one of the hottest topics in current genome research. The Bayesian Network (BN), which is a graph-based representation of a joint probability distribution that captures properties of conditional independence between variables, is a desirable tool to find the interaction between genes. However, how to find appropriate BNs that most fit to the data is very difficult since the number of possible BNs on n variables is the super-exponential of n. To avert the combinational explosion, in this paper, we use estimation of distribution algorithm (EDA) to search the BN space. Also, in order to make the individuals of EDA meaningful, we also propose a depth-first search method to cut circles in the graph. We have tested our method on cell-cycle gene expression data, the results show that the constructed BNs can not only discover some existing relationships in other literatures and Gene Ontology, but also reveal some previously unknown interactions.

关键词： Bayesian network gene-gene interaction estimation of distribution algorithm

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