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The univariate marginal distribution algorithm Copes Well with Deception and Epistasis 1

20th European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP)

作者： Doerr, Benjamin Krejca, Martin S. Inst Polytech Paris Lab Informat LIX Ecole Polytech CNRS Palaiseau France Univ Potsdam Hasso Plattner Inst Potsdam Germany

ISBN: (数字)9783030436803

ISBN: (纸本)9783030436797;9783030436803

In their recent work, Lehre and Nguyen (FOGA 2019) show that the univariate marginal distribution algorithm (UMDA) needs time exponential in the parent populations size to optimize the DeceivingLeadingBlocks (DLB) problem. They conclude from this result that univariate EDAs have difficulties with deception and epistasis. In this work, we show that this negative finding is caused by an unfortunate choice of the parameters of the UMDA. When the population sizes are chosen large enough to prevent genetic drift, then the UMDA optimizes the DLB problem with high probability with at most lambda(n(2) +2e ln n) fitness evaluations. Since an offspring population size lambda of order n log n can prevent genetic drift, the UMDA can solve the DLB problem with O(n(2) log n) fitness evaluations. In contrast, for classic evolutionary algorithms no better run time guarantee than O(n(3)) is known, so our result rather suggests that the UMDA can cope well with deception and epistatis. Together with the result of Lehre and Nguyen, our result for the first time rigorously proves that running EDAs in the regime with genetic drift can lead to drastic performance losses.

关键词： Estimation-of-distribution algorithm univariate marginal distribution algorithm Run time analysis Epistasis Theory

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On the Limitations of the univariate marginal distribution algorithm to Deception and Where Bivariate EDAs might help 19

On the Limitations of the Univariate Marginal Distribution A...

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15th ACM/SIGEVO Conference on Foundations of Genetic algorithms (FOGA)

作者： Lehre, Per Kristian Phan Trung Hai Nguyen Univ Birmingham Sch Comp Sci Birmingham W Midlands England

ISBN: (纸本)9781450362542

We introduce a new benchmark problem called Deceptive Leading Blocks (DLB) to rigorously study the runtime of the univariate marginal distribution algorithm (UMDA) in the presence of epistasis and deception. We show that simple Evolutionary algorithms (EAs) outperform the UMDA unless the selective pressure mu/lambda is extremely high, where mu and lambda are the parent and offspring population sizes, respectively. More precisely, we show that the UMDA with a parent population size of mu = Q (log n) has an expected runtime of e(Omega(mu)) on the DLB problem assuming any selective pressure mu/lambda >= 14/1000, as opposed to the expected runtime of O (n lambda log lambda + n(3)) for the non-elitist (mu, lambda) EA with mu/lambda <= 1/e. These results illustrate inherent limitations of univariate EDAs against deception and epistasis, which are common characteristics of real-world problems. In contrast, empirical evidence reveals the efficiency of the bi-variate MIMIC algorithm on the DLB problem. Our results suggest that one should consider EDAs with more complex probabilistic models when optimising problems with some degree of epistasis and deception.

关键词： Running time analysis univariate marginal distribution algorithm deception epistasis deceptive leading blocks theory

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A novel Gaussian matched filter based on entropy minimization for automatic segmentation of coronary angiograms

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COMPUTERS & ELECTRICAL ENGINEERING 2016年 53卷 263-275页

作者： Cruz-Aceves, Ivan Cervantes-Sanchez, Fernando Hernandez-Aguirre, Arturo Perez-Rodriguez, Ricardo Ochoa-Zezzatti, Alberto Ctr Invest Matemat CIMAT AC Jalisco S-N Guanajuato 36000 Gto Mexico Juarez City Univ Av Hero Colegio Mil S-N Ciudad Juarez 88700 Chihuahua Mexico

This paper presents a new method for automatic detection and segmentation of coronary arteries in X-ray angiograms. In the vessel detection stage, a novel Gaussian matched filter (GMF) based on an entropy minimization fitness function is used to detect blood vessels in angiographic images. The detection results of the proposed Gaussian matched filter are compared with those obtained by five state-of-the-art GMF-based methods using the area (A(z)) under the receiver operating characteristic (ROC) curve. In the second stage, the interclass variance thresholding method has proven to be the most efficient compared with six different methods in order to classify vessel and non vessel pixels from the Gaussian filter response using the accuracy measure and the ground-truth angiograms drawn by a specialist. Finally, the proposed method is compared with eight state-of-the-art vessel segmentation methods. Due to the high rating of similarity (0.97) between the highest A(z) value and the A(z) value acquired by the fitness function over the whole dataset of angiograms, the result of vessel detection using, the proposed GMF demonstrated high performance achieving A(z) = 0.945 with a test set of 45 angiograms. In addition, the results of vessel segmentation with the inter-class variance thresholding method provided an accuracy of 0.961 with the test set of angiograms. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Automatic segmentation Coronary arteries Entropy Gaussian matched filters univariate marginal distribution algorithm Vessel detection

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The compact genetic algorithm

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 1999年第4期3卷 287-297页

作者： Harik, GR Lobo, FG Goldberg, DE Univ Illinois Dept Gen Engn Illinois Genet Algorithms Lab Urbana IL 61801 USA

This paper introduces the compact genetic algorithm (cGA) which represents the population as a probability distribution over the set of solutions and is operationally equivalent to the order-one behavior of the simple GA with uniform crossover. It processes each gene independently and requires less memory than the simple GA. The development of the compact GA is guided by a proper understanding of the role of the GA's parameters and operators. The paper clearly illustrates the mapping of the simple GA's parameters into those of an equivalent compact GA;Computer simulations compare both algorithms in terms of solution quality and speed. Finally, this work raises important questions about the use of information in a genetic algorithm, and its ramifications show us a direction that can lead to the design of more efficient GA's.

关键词： bit wise simulated crossover genetic algorithms population based incremental learning probabilistic modeling univariate marginal distribution algorithm

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A comparative study of heuristic algorithms: GA and UMDA in spatially multiplexed communication systems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2010年第1期23卷 95-101页

作者： Bashir, Sajid Naeem, Muhammad Shah, Syed Ismail Ctr Adv Studies Engn Islamabad Pakistan Simon Fraser Univ Burnaby BC V5A 1S6 Canada Iqra Univ Islamabad Pakistan

A performance comparison of genetic algorithm (GA) and the univariate marginal distribution algorithm (UMDA) as decoders in multiple input multiple output (MIMO) communication system is presented in this paper. While the optimal maximum likelihood (ML) decoder using an exhaustive search method is prohibitively complex, simulation results show that the CA and UMDA optimized MIMO detection algorithms result in near optimal bit error rate (BER) performance with significantly reduced computational complexity. The results also suggest that the heuristic based MIMO detection outperforms the vertical bell labs layered space time (VBLAST) detector without severely increasing the detection complexity. The performance of UMDA is found to be superior to that of CA in terms of computational complexity and the BER performance. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Computational complexity Detection Genetic algorithm Multiple input multiple output univariate marginal distribution algorithm Vertical bell labs space time architecture

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Hybrid estimation of distribution algorithms for solving a keyboard layout problem

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2018年第6期35卷 352-367页

作者： Pradeepmon, T. G. Panicker, Vinay V. Sridharan, R. Natl Inst Technol Dept Mech Engn Calicut Kerala India

The use of smartphones and handheld devices in our daily activities has sharply increased. The added features of wireless technology and related applications on these devices make it possible to write emails, notes, and long text. Even though the most commonly used electronic input device is a keyboard, very little work has been dedicated for finding an optimal layout for this device. In this research, the aim is to propose a better layout for the single-finger keyboard in terms of rapid typing. The keyboard layout problem can be formulated as a quadratic assignment problem, which is one of the hardest combinatorial optimization problems. Some well-known literary works in English are chosen for estimating the keying-in-time. A variant of genetic algorithm, namely, the estimation of distribution algorithms is used to find a better layout. The new layout is found to be efficient compared with some of the existing prominent keyboard layouts.

关键词： Single-finger keyboard estimation of distribution algorithm quadratic assignment problem keyboard layout problem population based incremental learning univariate marginal distribution algorithm

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Hybridization of Evolutionary Mechanisms for Feature Subset Selection in Unsupervised Learning

Hybridization of Evolutionary Mechanisms for Feature Subset ...

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8th Mexican International Conference on Artificial Intelligence

作者： Torres, Dolores Ponce-de-Leon, Eunice Torres, Aurora Ochoa, Alberto Diaz, Elva Univ Autonoma Aguascalientes Ctr Ciencias Basicas Ave Univ940 Col Ciudad Univ Aguascalientes 20100 Ags Mexico Univ Autonoma Ciudad Juarez Inst Ingn & Tecnol Dept Ingn Elect & Computac Chamizal 32310 Mexico

ISBN: (纸本)9783642052576

Feature subset selection for unsupervised learning, is a very important topic in artificial intelligence because it is the base for saving computational resources. In this implementation we use a typical testor's methodology in order to incorporate an importance index for each variable. This paper presents the general framework and the way two hybridized meta-heuristics work in this NP-complete problem. The evolutionary mechanisms are based on the univariate marginal distribution algorithm (UMDA) and the Genetic algorithm (GA). GA and UMDA Estimation of distribution algorithm (EDA) use a very useful rapid operator implemented for finding typical testors on a very large dataset and also, both algorithms, have a local search mechanism for improving time and fitness. Experiments show that EDA is faster than GA because it has a better exploitation performance;nevertheless. GA' solutions are more consistent.

关键词： Hybridized Evolutionary Mechanisms Feature Subset Selection univariate marginal distribution algorithm Genetic algorithm Unsupervised Learning

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A co-evolutionary approach to interpretable reinforcement learning in environments with continuous action spaces

A co-evolutionary approach to interpretable reinforcement le...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Custode, Leonardo Lucio Iacca, Giovanni Univ Trento Dept Informat Engn & Comp Sci Trento Italy

ISBN: (纸本)9781728190488

Machine learning (ML) has lately achieved impressive breakthroughs in several fields, enabling a plethora of exciting applications. However, mainstream ML techniques often have an undesirable property: they are not directly understandable by humans, thus humans cannot trust them in high-stakes or life-critical scenarios. A subfield of AI called interpretable AI (IAI) addresses this problem: generating models that are easy to understand for humans and, consequently, trustworthy. While several approaches apply IAI techniques to reinforcement learning problems, addressing the case in which an agent has to act in a continuous action space is still an open question. In this work, we propose a cooperative co-evolutionary approach based on grammatical evolution, Q-learning, and the univariate marginal distribution algorithm, specifically designed to train IAI agents (in the form of binary decision trees) capable of acting in environments with continuous action spaces. The experimental results show that our method is able to solve two well-known OpenAI Gym test cases reaching state-of-the-art performance. Moreover, a quantitative post hoc analysis reveals that the obtained solutions are more interpretable than those reported in the literature.

关键词： Grammatical Evolution univariate marginal distribution algorithm Q-Learning Interpretability OpenAI Gym

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A UMDA-Based Discretization Method for Continuous Attributes

A UMDA-Based Discretization Method for Continuous Attributes

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7th International Conference on MEMS, NANO and Smart Systems (ICMENS 2011)

作者： Zhao Jing Han ChongZhao Wei Bin Han DeQiang Xi An Jiao Tong Univ Sch Elect & Informat Engn Minist EducKey Lab Intelligent Networks & Networ Inst Integrated AutomatState Key Lab Mfg Syst En Xian 710049 Peoples R China Xi An Jiao Tong Univ Inst Syst Engc Elect Informat Engn Sch Xian 710049 Peoples R China

ISBN: (纸本)9783037853122

Discretization of continuous attributes have played an important role in machine learning and data mining. They can not only improve the performance of the classifier, but also reduce the space of the storage. univariate marginal distribution algorithm is a modified Evolutionary algorithms, which has some advantages over classical Evolutionary algorithms such as the fast convergence speed and few parameters need to be tuned. In this paper, we proposed a bottom-up, global, dynamic, and supervised discretization method on the basis of univariate marginal distribution algorithm. The experimental results showed that the proposed method could effectively improve the accuracy of classifier.

关键词： Discretization univariate marginal distribution algorithm Data mining

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Comparing Solution Combination Techniques in Scatter Search for Quadratic Unconstrained Binary Optimization

Comparing Solution Combination Techniques in Scatter Search ...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Pauckert, Justin Debevere, Pieter Parizy, Matthieu Ayodele, Mayowa Fujitsu Ltd Kawasaki Kanagawa Japan Fujitsu Res Europe Slough Berks England Tech Univ Berlin Berlin Germany

ISBN: (纸本)9798400701207

Quadratic Unconstrained Binary Optimization (QUBO) has emerged as a vital unifying model for combinatorial optimization problems, and (meta-)heuristic approaches are commonly used to solve them due to their NP-hard nature. Scatter Search (SS), a populationbased metaheuristic framework, is one such method that has shown promising results for QUBO problems. Generating new solutions from more promising ones is a crucial operation in SS. Path Relinking (PR) based SS has been previously used to solve challenging QUBO problems with high-quality solutions. This paper introduces two new variants of the SS algorithm. The first is the (Multi) Uniform Crossover (MUC) based SS while the second is the univariate marginal distribution algorithm (UMDA) based SS. MUC and UMDA are well-known operators in Genetic algorithms and Estimation of distribution algorithms respectively. When compared to the existing PR based SS, this work shows that more promising results can be achieved when the newly proposed MUC and UMDA-based SS are applied to QUBO formulations of the Quadratic Knapsack Problem (QKP) instances.

关键词： QUBO Scatter Search Multi Uniform Crossover Path Relinking univariate marginal distribution algorithm Tabu Search Rust Programming Language

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