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26th European Conference on Genetic Programming (EuroGP) Held as Part of EvoStar

作者： Wittenberg, David Rothlauf, Franz Johannes Gutenberg Univ Mainz Mainz Germany

ISBN: (纸本)9783031295720;9783031295737

Denoising Autoencoder Genetic Programming (DAE-GP) is a model-based evolutionary algorithm that uses denoising autoencoder long short-term memory networks as probabilistic model to replace the standard recombination and mutation operators of genetic programming (GP). In this paper, we use the DAE-GP to solve a set of nine standard real-world symbolic regression tasks. We compare the prediction quality of the DAE-GP to standard GP, geometric semantic GP (GSGP), and the gene-pool optimal mixing evolutionary algorithm for GP (GOMEA-GP), and find that the DAE-GP shows similar prediction quality using a much lower number of fitness evaluations than GSGP or GOMEA-GP. In addition, the DAE-GP consistently finds small solutions. The best candidate solutions of the DAE-GP are 69% smaller (median number of nodes) than the best candidate solutions found by standard GP. An analysis of the bias of the selection and variation step for both the DAE-GP and standard GP gives insight into why differences in solution size exist: the strong increase in solution size for standard GP is a result of both selection and variation bias. The results highlight that learning and sampling from a probabilistic model is a promising alternative to classic GP variation operators where the DAE-GP is able to generate small solutions for real-world symbolic regression tasks.

关键词： Genetic Programming estimation of distribution algorithms Denoising Autoencoders Symbolic Regression

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ECJ at 20: Toward a General Metaheuristics Toolkit 19

ECJ at 20: Toward a General Metaheuristics Toolkit

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Scott, Eric O. Luke, Sean George Mason Univ Washington DC 22030 USA

ISBN: (纸本)9781450367486

ECJ is now 20 years old. Begun as a genetic programming and evolutionary computation library in Java, it has since established itself as historically one of the most popular EC toolkits worldwide. In 2016 we received a National Science Foundation grant to improve ECJ in many ways with an eye toward making it a useful toolkit not just for EC but for the broader metaheuristics community. This paper is a report on our efforts to this end. We discuss new metaheuristics frameworks and representations added to ECJ and the design challenges that they raise for a general-purpose framework, as well as testing facilities and other support tools. We conclude with our future directions for the library.

关键词： Metaheuristics Evolutionary algorithms Ant Colony Optimization estimation of distribution algorithms

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Hybrid Feature Selection and Peptide Binding Affinity Prediction using an EDA based Algorithm

Hybrid Feature Selection and Peptide Binding Affinity Predic...

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IEEE Congress on Evolutionary Computation

作者： Shelke, Kalpesh Jayaraman, Srikant Ghosh, Shameek Valadi, Jayaraman Univ Pune Ctr Modeling & Simulat Pune Maharashtra India

ISBN: (纸本)9781479904549;9781479904532

Protein function prediction is an important problem in functional genomics. Typically, protein sequences are represented by feature vectors. A major problem of protein datasets that increase the complexity of classification models is their large number of features. The process of drug discovery often involves the use of quantitative structure-activity relationship (QSAR) models to identify chemical structures that could have good inhibitory effects on specific targets and have low toxicity (non-specific activity). QSAR models are regression or classification models used in the chemical and biological sciences. Because of high dimensionality problems, a feature selection problem is imminent. In this study, we thus employ a hybrid estimation of distribution Algorithm (EDA) based filter-wrapper methodology to simultaneously extract informative feature subsets and build robust QSAR models. The performance of the algorithm was tested on the benchmark classification challenge datasets obtained from the CoePRa competition platform, developed in 2006. Our results clearly demonstrate the efficacy of a hybrid EDA filter-wrapper algorithm in comparison to the results reported earlier.

关键词： Feature Selection Protein Function Prediction estimation of distribution algorithms Weighted Feature Ranking

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A Bayesian based Hyper-Heuristic approach for global optimization

A Bayesian based Hyper-Heuristic approach for global optimiz...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Oliva, Diego Martins, Marcella S. R. Univ Guadalajara CUCEI Dept Ciencias Computac Guadalajara Jalisco Mexico Univ Tecnol Fed Parana Dept Elect Engn DAELE Ponta Grossa Parana Brazil

ISBN: (纸本)9781728121536

Several metaheuristics have been developed for global optimization. Most of them are designed for solving a specific problem at hand, and their use on a new implementation is a challenging task. Hyper-heuristics are strategies that support these issues, combining a metaheuristic in a high-level for selecting or generating simple heuristics from a low-level. The aim is to find nearoptimal solutions based on the feedback received during the search. estimation of distribution algorithms (EDAs) have been applied as hyper-heuristics, using a Probabilistic Graphical Model (PGM) to extract and represent interactions between its low-level heuristics to provide high-valued problem solutions. In this paper, we consider an EDA based on Bayesian networks as PGM on a hyper-heuristic context which encompasses a heuristic selection approach to find the best combinations of different known simple heuristics. We compare our proposed approach named Hyper-heuristic approach based on Bayesian Optimization Algorithm (HHBOA) using CEC'05 benchmark functions among 9 optimization algorithms. The experimental results show that HHBOA is competitive, outperforming the other approaches, especially in terms of convergence, on most of the functions considered in this paper.

关键词： Global continuous optimization estimation of distribution algorithms hyper-heuristics bayesian networks

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A hybrid evolutionary approach for set packing problem

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OPSEARCH 2015年第2期52卷 271-284页

作者： Chaurasia, Sachchida Sundar, Shyam Singh, Alok Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500046 Andhra Pradesh India Natl Inst Technol Raipur Dept Comp Applicat Raipur 492010 Madhya Pradesh India

In this paper, we present a hybrid approach comprising an evolutionary algorithm with guided mutation (EA/G) and a local search to solve the set packing problem (SPP). EA/G is a recently proposed evolutionary algorithm that can be considered as a cross between genetic algorithms (GAs) and estimation of distribution algorithms (EDAs) and that tries to overcome the shortcomings of both. Guided mutation in EA/G generates offsprings through a probability model based on a combination of global statistical information and location information of the solutions found so far. We have compared our approach with the state-of-the-art approaches. Computational results show the effectiveness of our approach.

关键词： Constrained optimization estimation of distribution algorithms Evolutionary algorithms Guided mutation Set packing problem

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Research on Methodology of Evolutionary Computing

Research on Methodology of Evolutionary Computing

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2nd International Conference on Mechatronics and Intelligent Materials (MIM 2012)

作者： Zhuo, Junfei Wu, Xinghe Wu, Guanzhao Yao, Min Zhejiang Univ Ctr Comp Hangzhou 310027 Zhejiang Peoples R China Zhejiang Univ Coll Comp Sci Hangzhou 310027 Peoples R China

Evolutionary computing is one of the important branches in computational intelligence. This paper mainly introduces four new branches of the evolutionary computation, i.e. Gene Expression Programming (GEP), Particle S... 详细信息

ISBN: (纸本)9783037853849

关键词： Evolutionary computing gene expression programming particle swarm optimization differential evolution estimation of distribution algorithms

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Using a Markov network model in a univariate EDA: An empirical cost-benefit analysis 05

Using a Markov network model in a univariate EDA: An empiric...

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Genetic and Evolutionary Computation Conference

作者： Shakya, Siddhartha McCall, John Brown, Deryck Robert Gordon Univ Sch Comp Aberdeen AB9 1FR Scotland

ISBN: (纸本)1595930108

This paper presents an empirical cost-benefit analysis of an algorithm called distribution estimation Using MRF with direct sampling (DEUMd). DEUMd belongs to the family of estimation of distribution Algorithm (EDA). Particularly it is a univariate EDA. DEUMd uses a computationally more expensive model to estimate the probability distribution than other univariate EDAs. We investigate the performance of DEUMd in a range of optimization problem. Our experiments shows a better performance (in terms of the number of fitness evaluation needed by the algorithm to find a solution and the quality of the solution) of DEUMd on most of the problems analysed in this paper in comparison to that of other univariate EDAs. We conclude that use of a Markov Network in a univariate EDA can be of net benefit in defined set of circumstances.

关键词： estimation of distribution algorithms evolutionary computation probabilistic modelling

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Cross Entropy and Adaptive Variance Scaling in Continuous EDA 07

Cross Entropy and Adaptive Variance Scaling in Continuous ED...

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Annual Conference of Genetic and Evolutionary Computation Conference

作者： Cai Yunpeng Sun Xiaomin Xu Hua Jia Peifa Tsinghua Univ State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China

ISBN: (纸本)9781595936974

This paper deals with the adaptive variance scaling issue in continuous estimation of distribution algorithms. A phenomenon is discovered that current adaptive variance scaling method in EDA suffers front imprecise structure learning. A new type of adaptation method is proposed to overcome this defect. The method tries to measure the difference between the obtained population and the prediction of the probabilistic model, then calculate the scaling factor by minimizing the cross entropy between these two distributions. This approach calculates the scaling factor immediately rather than adapts it incrementally. Experiments show that this approach extended the class of problems that can be solved, and improve the search efficiency in some cases. Moreover, the proposed approach features in that each decomposed subspace call be assigned all individual scaling factor, which helps to solve problems with special dimension property.

关键词： Evolutionary Computation estimation of distribution algorithms Adaptive Variance Scaling Cross Entropy

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Enhancing the Efficiency of the ECGA

Enhancing the Efficiency of the ECGA

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10th International Conference on Parallel Problem Solving from Nature

作者： Duque, Thyago S. P. C. Goldberg, David E. Sastry, Kumara Univ Illinois Illinois Genet Algorithms Lab Urbana IL 61801 USA

ISBN: (纸本)9783540876991

In this paper we show preliminary results of two efficiency enhancements proposed for Extended Compact Genetic Algorithm. First, a model building enchancement 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 local-search 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 are the first steps toward a competent and efficient Genetic Algorithm.

关键词： estimation of distribution algorithms ECGA Model Building Efficiency Enhancement

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The Asteroid Routing Problem: A Benchmark for Expensive Black-Box Permutation Optimization 25th

The Asteroid Routing Problem: A Benchmark for Expensive Blac...

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25th International Conference on the Applications of Evolutionary Computation (EvoApplications) Held as Part of EvoStar Conference

作者： Lopez-Ibanez, Manuel Chicano, Francisco Gil-Merino, Rodrigo Univ Malaga ITIS Software Malaga Spain

ISBN: (纸本)9783031024627;9783031024610

Inspired by the recent 11th Global Trajectory Optimisation Competition, this paper presents the asteroid routing problem (ARP) as a realistic benchmark of algorithms for expensive bound-constrained black-box optimization in permutation space. Given a set of asteroids' orbits and a departure epoch, the goal of the ARP is to find the optimal sequence for visiting the asteroids, starting from Earth's orbit, in order to minimize both the cost, measured as the sum of the magnitude of velocity changes required to complete the trip, and the time, measured as the time elapsed from the departure epoch until visiting the last asteroid. We provide open-source code for generating instances of arbitrary sizes and evaluating solutions to the problem. As a preliminary analysis, we compare the results of two methods for expensive blackbox optimization in permutation spaces, namely, Combinatorial Efficient Global Optimization (CEGO), a Bayesian optimizer based on Gaussian processes, and Unbalanced Mallows Model (UMM), an estimation-of-distribution algorithm based on probabilistic Mallows models. We investigate the best permutation representation for each algorithm, either rank-based or order-based. Moreover, we analyze the effect of providing a good initial solution, generated by a greedy nearest neighbor heuristic, on the performance of the algorithms. The results suggest directions for improvements in the algorithms being compared.

关键词： Spacecraft trajectory optimization Unbalanced Mallows Model Combinatorial Efficient Global Optimization estimation of distribution algorithms Bayesian optimization

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