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Wavelength Assignment for Physical-Layer-Impaired Optical Networks Using evolutionary computation

JOURNAL OF OPTICAL COMMUNICATIONS AND NETWORKING 2011年第3期3卷 178-188页

作者： Bastos-Filho, Carmelo J. A. Chaves, Daniel A. R. Silva, Felipe S. F. e Pereira, Helder A. Martins-Filho, Joaquim F. Univ Fed Pernambuco Polytech Sch Pernambuco BR-52720001 Recife PE Brazil Univ Fed Pernambuco Dept Elect Syst BR-50740530 Recife PE Brazil

This paper presents a wavelength assignment algorithm suitable for optical networks mainly impaired by physical layer effects, named the Intelligent Wavelength Assignment algorithm (iWA). The main idea is to determine the wavelength activation order for a first-fit algorithm that balances the impact of the physical layer effects by using a training algorithm inspired by evolutionary concepts. The iWA presents some recently proposed concepts in intelligent optimization algorithms, such as an external archive to store the best solutions. Some different physical layer effects, such as four-wave mixing and residual dispersion, were considered in the tests of our proposal. We tested our proposal for transparent optical networks. However, we believe iWA can be used in other types of network, such as opaque networks and translucent networks. The proposed wavelength assignment algorithm was compared with five other wavelength assignment algorithms for two network topologies in three different scenarios. The iWA algorithm outperformed the other WA algorithms in most cases. The robustness of our proposed algorithm to the load distribution changes was also analyzed.

关键词： evolutionary computation IA-RWA IA-WA Optical networks Physical impairments RWA Wavelength assignment algorithms

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ECoFFeS: A Software Using evolutionary computation for Feature Selection in Drug Discovery

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IEEE ACCESS 2018年 6卷 20950-20963页

作者： Liu, Zhi-Zhong Huang, Jia-Wei Wang, Yong Cao, Dong-Sheng Cent South Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England Cent South Univ Xiangya Sch Pharmaceut Sci Changsha 410013 Hunan Peoples R China

Feature selection is of particular importance in the field of drug discovery. Many methods have been put forward for feature selection during recent decades. Among them, evolutionary computation has gained increasing attention owing to its superior global search ability. However, there still lacks a simple and effi cient software for drug developers to take advantage of evolutionary computation for feature selection. To remedy this issue, in this paper, a user-friendly and standalone software, named ECoFFeS, is developed. ECoFFeS is expected to lower the entry barrier for drug developers to deal with feature selection problems at hand by using evolutionary algorithms. To the best of our knowledge, it is the fi rst software integrating a set of evolutionary algorithms (including two modi fi ed evolutionary algorithms proposed by the authors) with various evaluation combinations for feature selection. Specifi cally, ECoFFeS considers both single-objective and multi-objective evolutionary algorithms, and both regression-and classification-based models to meet different requirements. Five data sets in drug discovery are collected in ECoFFeS. In addition, to reduce the total analysis time, the parallel execution technique is incorporated into ECoFFeS. The source code of ECoFFeS can be available from https://***/JiaweiHuang/ECoFFeS/.

关键词： evolutionary computation feature selection drug discovery single-objective optimization multi-objective optimization parallel execution

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Comparison of evolutionary computation techniques for noise injected neural network training to estimate longitudinal dispersion coefficients in rivers

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第1期39卷 1354-1361页

作者： Piotrowski, Adam P. Rowinski, Pawel M. Napiorkowski, Jaroslaw J. Polish Acad Sci Inst Geophys PL-01452 Warsaw Poland

This study presents the comparison of various evolutionary computation (EC) optimization techniques applied to train the noise-injected multi-layer perceptron neural networks used for estimation of longitudinal dispersion coefficient in rivers. The special attention is paid to recently developed variants of Differential Evolution (DE) algorithm. The most commonly used gradient-based optimization methods have two significant drawbacks: they cannot cope with non-differentiable problems and quickly converge to local optima. These problems can be avoided by the application of EC techniques. Although a great amount of various EC algorithms have been proposed in recent years, only some of them have been applied to neural network training - usually with no comparison to other methods. We restrict our comparison to the regression problem with limited data and noise injection technique used to avoid premature convergence and to improve robustness of the model. The optimization methods tested in the present paper are: Distributed DE with Explorative-Exploitative Population Families, Self-Adaptive DE, DE with Global and Local Neighbors, Grouping DE, JADE, Comprehensive Learning Particle Swarm Optimization, Efficient Population Utilization Strategy Particle Swarm Optimization and Covariance Matrix Adaptation - Evolution Strategy. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Differential Evolution Particle Swarm Optimization Evolution Strategy Neural Networks evolutionary computation Longitudinal dispersion Noise injection

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History-Guided Hill Exploration for evolutionary computation

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IEEE TRANSACTIONS ON evolutionary computation 2023年第6期27卷 1962-1975页

作者： Wang, Junchen Li, Changhe Zeng, Sanyou Yang, Shengxiang China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat Co Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China China Univ Geosci Sch Mech Engn & ElectronicInformat Wuhan 430074 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH England

Although evolutionary computing (EC) methods are stochastic optimization methods, it is usually difficult to find the global optimum by restarting the methods when the population converges to a local optimum. A major reason is that many optimization problems have basins of attraction (BoAs) that differ widely in shape and size, and the population always prefers to converge toward BoAs that are easy to search. Although heuristic restart based on tabu search is a theoretically feasible idea to solve this problem, existing EC methods with heuristic restart are difficult to avoid repetitive search results while maintaining search efficiency. This article tries to overcome the dilemma by online learning the BoAs and proposes a search mode called history-guided hill exploration (HGHE). In the search mode, evaluated solutions are used to help separate the search space into hill regions which correspond to the BoAs, and a classical EC method is used to locate the optimum in each hill region. An instance algorithm for continuous optimization named HGHE differential evolution (HGHE-DE) is proposed to verify the effectiveness of HGHE. Experimental results prove that HGHE-DE can continuously discover unidentified BoAs and locate optima in identified BoAs.

关键词： Basin of attraction (BoA) evolutionary computation heuristic restart space partitioning

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Improving experimental methods on success rates in evolutionary computation

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2017年第4期29卷 695-716页

作者： Barrero, David F. R-Moreno, Maria D. Camacho, David Univ Alcala Comp Engn Dept Alcala De Henares Spain Univ Autonoma Madrid Escuela Politecn Super Madrid Spain

Due to the complexity of theoretical approaches in evolutionary computation (EC), research has being largely performed on experimental basis. One popular measure used by the EC community is the success rate (SR), which is used alone or as part of more complex measures such as Koza's computational effort in genetic programming. A common practice in EC is to report just a punctual estimation of the SR, without additional information about its associated uncertainty. We aim to motivate EC researchers to adopt more rigorous practices when working with SRs. In particular, we introduce the importance of correctly reporting this measure and highlight its binomial nature. Unfortunately, this fact is usually overlooked in the literature. Considering the binomiality of the SR opens the whole corpus of binomial statistics to EA research and practice. In particular, we focus on studying several methods to compute SR confidence intervals, the factors that determine their quality in terms of coverage probability and interval length. Due to its practical interest, we also briefly discuss the number of required runs to build confidence intervals with a certain quality, providing a sound method to set the number of runs, one of the most important experimental settings in EC. Evidence suggests that Wilson is, on average, a reliable and simple method to bound an estimation of SR with confidence intervals, while the standard method, which is quite popular because of its conceptual simplicity, should be avoided in any case. However, other methods can also be of interest under certain circumstances. We encourage to report the number of trials and successes, as well as the interval, to ease further comparability of the results.

关键词： Experimentation success rate confidence intervals genetic programming evolutionary computation performance measures

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A review on probabilistic graphical models in evolutionary computation

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JOURNAL OF HEURISTICS 2012年第5期18卷 795-819页

作者： Larranaga, Pedro Karshenas, Hossein Bielza, Concha Santana, Roberto Univ Politecn Madrid Fac Informat Computat Intelligence Grp E-28660 Madrid Spain Univ Basque Country Dept Comp Sci & Artificial Intelligence Intelligent Syst Grp San Sebastin 20080 Donostia Spain

Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. evolutionary algorithms is one such discipline that has employed probabilistic graphical models to improve the search for optimal solutions in complex problems. This paper shows how probabilistic graphical models have been used in evolutionary algorithms to improve their performance in solving complex problems. Specifically, we give a survey of probabilistic model building-based evolutionary algorithms, called estimation of distribution algorithms, and compare different methods for probabilistic modeling in these algorithms.

关键词： Probabilistic graphical model Bayesian network evolutionary computation Estimation of distribution algorithm

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A new irregular cellular learning automata-based evolutionary computation for time series link prediction in social networks

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APPLIED INTELLIGENCE 2021年第1期51卷 71-84页

作者： Manshad, Mozhdeh Khaksar Meybodi, Mohammad Reza Salajegheh, Afshin Islamic Azad Univ South Tehran Branch Dept Comp Engn Tehran Iran Amirkabir Univ Technol Dept Comp Engn Tehran Iran

Link prediction (LP), as an attempt to predict event-based future connections within a network, is the main task of social network analysis (SNA). Accordingly, common LP approaches to forecast future connections utilize similarity metrics of non-connected links in a static network representation. A general shortcoming of most existing research studies in this field is that they tap the present condition of a system and fail to take any temporal events into account. Moreover;social networks are innately evolutionary since they are assumed to be online, non-deterministic, and unforeseeable in most applications. Consequently, it is not appropriate to employ deterministic models for examining actual social network problems. With regard to time-series LP (TSLP) problems, temporal evolution of connection incidence is correspondingly exploited to predict connection chances at a particular time. In this paper, a new TSLP method based on irregular cellular learning automaton (ICLA) and evolutionary computation (EC) is proposed. In the evolutionary procedure suggested here, each vertex (i.e. cell) includes a genome as well as a set of learning automata (LAs). Accordingly, the genome residing in a cell represents predicted links for the corresponding cell. Local information among cells in successive time1toTin the network is then analyzed to predict future connections in timeT + 1. According to the distributed feature of the recommended approach, each genome is locally developed by a local search. The experiments in this study via e-mail and co-authorship networks ultimately show that the proposed algorithm leads to remarkable outcomes in predicting future connections.

关键词： Time-series link prediction Irregular cellular learning automata evolutionary computation

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A Survey on evolutionary computation Approaches to Feature Selection

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IEEE TRANSACTIONS ON evolutionary computation 2016年第4期20卷 606-626页

作者： Xue, Bing Zhang, Mengjie Browne, Will N. Yao, Xin Victoria Univ Wellington Evolutionary Computat Res Grp Wellington 6140 New Zealand Univ Birmingham Sch Comp Sci Nat Computat Grp Birmingham B15 2TT W Midlands England

Feature selection is an important task in data mining and machine learning to reduce the dimensionality of the data and increase the performance of an algorithm, such as a classification algorithm. However, feature selection is a challenging task due mainly to the large search space. A variety of methods have been applied to solve feature selection problems, where evolutionary computation (EC) techniques have recently gained much attention and shown some success. However, there are no comprehensive guidelines on the strengths and weaknesses of alternative approaches. This leads to a disjointed and fragmented field with ultimately lost opportunities for improving performance and successful applications. This paper presents a comprehensive survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms. In addition, current issues and challenges are also discussed to identify promising areas for future research.

关键词： Classification data mining evolutionary computation feature selection machine learning

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Grounding grid design using evolutionary computation-based methods

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2002年第2期30卷 151-165页

作者： Otero, AF Cidrás, J Garrido, C Univ Vigo Dept Enxeneria Elect Vigo Spain

In this paper, a new method for designing grounding grids by means of evolutionary computation techniques is proposed. The aim being pursued is to minimize the cost of the grounding system while meeting the safety restrictions required by the standard regulations. The utilization of evolutionary computation (in particular, genetic algorithms and evolution strategies) allows us to build unequally spaced networks that, as is well-known, produce a more uniform surface potential distribution than equally spaced networks.

关键词： Electric current grounding -- Equipment & supplies Electron tube grids evolutionary computation

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evolutionary music: applying evolutionary computation to the art of creating music

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GENETIC PROGRAMMING AND EVOLVABLE MACHINES 2020年第1-2期21卷 55-85页

作者： Loughran, Roisin O'Neill, Michael UCD Nat Comp Res & Applicat Grp NCRA Michael Smurfit Grad Business Sch Dublin Ireland

We present a review of the application of genetic programming (GP) and other variations of evolutionary computation (EC) to the creative art of music composition. Throughout the development of EC methods, since the early 1990s, a small number of researchers have considered aesthetic problems such as the act of composing music alongside other more traditional problem domains. Over the years, interest in these aesthetic or artistic domains has grown significantly. We review the implementation of GP and EC for music composition in terms of the compositional task undertaken, the algorithm used, the representation of the individuals and the fitness measure employed. In these aesthetic studies we note that there are more variations or generalisations in the algorithmic implementation in comparison to traditional GP experiments;even if GP is not explicitly stated, many studies use representations that are distinctly GP-like. We determine that there is no single compositional challenge and no single best evolutionary method with which to approach the act of music composition. We consider autonomous composition as a computationally creative act and investigate the suitability of EC methods to the search for creativity. We conclude that the exploratory nature of evolutionary methods are highly appropriate for a wide variety of compositional tasks and propose that the development and study of GP and EC methods on creative tasks such as music composition should be encouraged.

关键词： Music composition evolutionary computation computational creativity Review

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