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Genetic and evolutionary Computation Conference (GECCO)

作者： Sayar, Erdi Iacca, Giovanni Knoll, Alois Tech Univ Munich Munich Germany Univ Trento Trento Italy

ISBN: (纸本)9798400704949

Reinforcement learning (RL) algorithms often face challenges in efficiently learning effective policies for sparse-reward multi-goal robot manipulation tasks, thus requiring a vast amount of experiences. The state-of-the-art algorithm in the field, Hindsight Experience Replay (HER), addresses this issue by using failed trajectories and replacing the desired goal with hindsight goals. However, HER performs poorly when the desired goal is distant from the initial state. To address this limitation, Hindsight Goal Generation (HGG) has been proposed, which generates a curriculum of goals from already visited states. This curriculum generation is based on a single objective, and does not take obstacles into account. Here, we make a step forward by proposing multi-objective evolutionary Hindsight Experience Replay (MOEHER), a novel curriculum RL algorithm that reformulates curriculum generation considering multiple objectives and obstacles. MOEHER utilizes NSGA-II to generate a curriculum that is optimized w.r.t. four objectives, namely the Q-function, the goal-proximity function, and two distance metrics, while simultaneously satisfying constraints on the obstacles. We evaluate MOEHER on four different sparse-reward robot manipulation tasks, with and without obstacles, and compare it with HER and HGG. The results demonstrate that MOEHER surpasses or performs on par with these methods on the tested tasks.

关键词： multi-objective evolutionary algorithm Hindsight Experience Replay Reinforcement Learning Curriculum Learning Robot Manipulation

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Feature weighting for antimicrobial peptides classification: a multi-objective evolutionary approach

Feature weighting for antimicrobial peptides classification:...

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Biological Ontologies and Knowledge Bases Workshop at IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)

作者： Beltran, Jesus A. Aguilera-Mendoza, Longendri Brizuela, Carlos A. Ctr Sci Res & Higher Educ Ensenada CICESE Comp Sci Dept Ensenada 22860 Baja California Mexico

ISBN: (纸本)9781509030507

Antimicrobial peptides might become crucial in fighting antibiotic resistant bacteria and other infections. Next Generation Sequencing technologies are generating a large amount of data where peptides with antimicrobial activity could be found. Therefore, algorithms that can efficiently determine whether or not a short sequence of amino acids is antimicrobial are needed. In this context, Quantitative Structure-Activity Relationship modeling has paved the way toward the association of the physicochemical properties of peptides to their biological activity. Nowadays, there are algorithms that can compute thousands of physicochemical properties known as molecular descriptors. However, some of these descriptors are irrelevant and some might even mislead the correct classification of the peptide activity. To mitigate this problem, a descriptor selection process must be performed, this will help to improve the classification accuracy and to decrease the computational time required for classification. In a recent work, a general method to weight and select features has been proposed. The method models the descriptor selection problem as a multi-objective optimization problem (MOOP). The main idea is to optimize simultaneously the intra-and inter-class distances. We follow this approach and apply it to the feature selection problem for the classification of antimicrobial peptides. To this aim we modify the original MOOP formulation to avoid bringing together non-antimicrobial peptides. Preliminary results indicate that our approach can substantially reduce the number of required molecular descriptors and improve the performance of classification with respect to the original formulation.

关键词： antimicrobial peptides AMPs feature weighting molecular descriptors classification multi-objective evolutionary algorithm peptide representation

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An evolutionary algorithm-Based Text Categorization Technique 4th

An Evolutionary Algorithm-Based Text Categorization Techniqu...

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4th International Conference on Computational Intelligence in Data Mining (ICCIDM)

作者： Das, Ajit Kumar Das, Asit Kumar Sarkar, Apurba Indian Inst Engn Sci & Technol Dept Comp Sci & Technol Howrah 711103 W Bengal India

ISBN: (纸本)9789811080555;9789811080548

In general, most of the organizations generate unstructured data from which extraction of meaningful information becomes a difficult task. Preprocessing of unstructured data before mining helps to improve the efficiency of the mining algorithms. In this paper, text data is initially preprocessed using tokenization, stop word removal, and stemming operations and a bag-of-words is identified to characterize the text dataset. Next, improved strength pareto evolutionary algorithm-based genetic algorithm is applied to determine the more compact set of informative words for clustering of text documents efficiently. It is a bi-objective genetic algorithm used to approximate the pareto-optimal front exploring the search space for optimal solution. The external clustering index and number of words described in the documents are considered as two objective functions of the algorithm, and based on these functions chromosomes in the population are evaluated and the best chromosome in non dominated pareto front of final population gives the optimal set of words sufficient for categorizartion of text dataset.

关键词： Text mining Feature selection Text clustering Cluster validation multi-objective evolutionary algorithm

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multi-objective evolutionary Ensemble Pruning Guided by Margin Distribution 17th

Multi-objective Evolutionary Ensemble Pruning Guided by Marg...

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17th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Wu, Yu-Chang He, Yi-Xiao Qian, Chao Zhou, Zhi-Hua Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China

ISBN: (纸本)9783031147142;9783031147135

Ensemble learning trains and combines multiple base learners for a single learning task, and has been among the state-of-the-art learning techniques. Ensemble pruning tries to select a subset of base learners instead of combining them all, with the aim of achieving a better generalization performance as well as a smaller ensemble size. Previous methods often use the validation error to estimate the generalization performance during optimization, while recent theoretical studies have disclosed that margin distribution is also crucial for better generalization. Inspired by this finding, we propose to formulate ensemble pruning as a three-objective optimization problem that optimizes the validation error, margin distribution, and ensemble size simultaneously, and then employ multi-objective evolutionary algorithms to solve it. Experimental results on 20 binary classification data sets show that our proposed method outperforms the state-of-the-art ensemble pruning methods significantly in both generalization performance and ensemble size.

关键词： Machine learning Ensemble pruning multi-objective optimization Margin distribution multi-objective evolutionary algorithm

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Search Process Analysis of multiobjective evolutionary algorithms using Convergence-Diversity Diagram 12

Search Process Analysis of Multiobjective Evolutionary Algor...

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Joint 12th International Conference on Soft Computing and Intelligent Systems / 23rd International Symposium on Advanced Intelligent Systems (SCIS and ISIS)

作者： Kinoshita, Takato Masuyama, Naoki Nojima, Yusuke Osaka Prefecture Univ Guraduate Sch Engn Sakai Osaka Japan Osaka Metropolitan Univ Grad Sch Informat Sakai Osaka Japan

ISBN: (纸本)9781665499248

Many tasks in the real world are multi-objective optimization problems (MOPs). Population-based approaches are promising for solving MOPs. In particular, multi-objective evolutionary algorithms (MOEAs) are popular and have been actively studied over the last two decades. However, since it is not easy to directly display and compare multi-dimensional solution sets, it is difficult to analyze the search process of MOEAs using direct visualization techniques such as scatter plots. This paper proposes an analytical method to compare multiple search processes in terms of convergence and diversity by extending the authors' previous work, i.e., Convergence-Diversity Diagram. Through computational experiments, the proposed method reveals characteristics and similarities in three representative MOEAs and six test problems. In addition, this paper provides discussions on algorithm design, biases in the DTLZ test suite, and the improvement of visualization based on experimental results.

关键词： multi-objective optimization multi-objective evolutionary algorithm search process analysis convergence diversity

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multi-objective evolutionary Instance Selection for multi-label Classification 19th

Multi-objective Evolutionary Instance Selection for Multi-la...

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19th Pacific Rim International Conference on Artificial Intelligence (PRICAI)

作者： Liu, Dingming Shang, Haopu Hong, Wenjing Qian, Chao Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

ISBN: (纸本)9783031208614;9783031208621

multi-label classification is an important topic in machine learning, where each instance can be classified into more than one category, i.e., have a subset of labels instead of only one. Among existing methods, ML-kNN [25], the direct extension of k-nearest neighbors algorithm to the multi-label scenario, has received much attention due to its conciseness, great interpretability, and good performance. However, ML-kNN usually suffers from a terrible storage cost since all training instances need to be saved in the memory. To address this issue, a natural way is instance selection, intending to save the important instances while deleting the redundant ones. However, previous instance selection methods mainly focus on the single-label scenario, which may have a poor performance when adapted to the multi-label scenario. Recently, few works begin to consider the multi-label scenario, but their performance is limited due to the inapposite modeling. In this paper, we propose to formulate the instance selection problem for ML-kNN as a natural bi-objective optimization problem that considers the accuracy and the number of retained instances simultaneously, and adapt NSGA-II to solve it. Experiments on six real-world data sets show that our proposed method can achieve both not worse prediction accuracy and significantly better compression ratio, compared with state-of-the-art methods.

关键词： multi-label classification ML-kNN Instance selection multi-objective optimization multi-objective evolutionary algorithm

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A multi-objective Covariance Matrix Adaptation evolutionary Strategy based on Decomposition for Analog Circuit Design

A Multi-Objective Covariance Matrix Adaptation Evolutionary ...

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39th Annual Conference of the IEEE Industrial-Electronics-Society (IECON)

作者： Park, So-Youn Lee, Ju-Jang Korea Adv Inst Sci & Technol Dept Elect Engn Taejon 305701 South Korea

ISBN: (纸本)9781479902248

Sound localization has been utilized in many fields. For good performance, sound signal obtained from the microphone has to be well filtered and amplified. Although filtering and amplification can be implemented in digital, analog circuit is needed because of sampling aliasing problem and limitation of computational resources in real-time application. The analog circuit consists of several components. Because all of possible combinations cannot be investigated, evolutionary algorithm (EA) is chosen to find promising solution candidates. In the paper, we built a multi-objective covariance adaptation evolutionary strategy based on decomposition to obtain the component values. Through the experimental results, our proposed algorithm showed good performance.

关键词： State variable filter covariance matrix adaptation evolution strategy voltage level shifter circuit optimization multi-objective evolutionary algorithm scalarization

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Reliability-based optimization for multiple constraints with evolutionary algorithms

Reliability-based optimization for multiple constraints with...

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IEEE Congress on evolutionary Computation

作者： Daum, David A. Deb, Kalyanmoy Branke, Juergen Univ Karlsruhe Inst AIFB Karlsruhe Germany

ISBN: (纸本)9781424413393

In this paper, we combine reliability-based optimization with a multi-objective evolutionary algorithm for handling uncertainty in decision variables and parameters. This work is an extension to a previous study by the second author and his research group to more accurately compute a multi-constraint reliability. This means that the overall reliability of a solution regarding all constraints is examined, instead of a reliability computation of only one critical constraint. First, we present a brief introduction into this so-called 'structural reliability' aspects. Thereafter, we introduce a method for identifying inactive constraints according to the reliability evaluation. With this method, we show that with less number of constraint evaluations, an identical solution can be achieved. Furthermore, we apply our approach to a number of problems including a real-world car side impact design problem to illustrate our method.

关键词： evolutionary algorithm reliability evaluation Constraint multi-objective evolutionary algorithm Side impact

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Quality of Services based routing using evolutionary algorithms for Wireless Sensor Network 15

Quality of Services based routing using Evolutionary Algorit...

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15th International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Hajjej, Faten Ejbali, Ridha Zaied, Mourad Res Lab Intelligent Machines REGIM Lab Sfax Tunisia

ISBN: (纸本)9781467387095

In this paper we invoke a new approach for the multi-objective routing problems in Wireless Sensor Networks (WSNs). Our approach improves more than one single Quality of Services (QoS) exigency such as energy consumption and delay. However, the classical routing protocols in conventional network optimize a single objective or QoS parameters. The proposed approach adapted a multi-objective evolutionary algorithm (MOEA), specifically, the improved Strength Pareto evolutionary algorithm (SPEA2) in order to improve the QoS in WSNs. Our simulation results show that the SPEA2 algorithms are efficient in solving routing problems and are capable of finding the Pareto optimal Set. Additionally, we demonstrate that this approach provides better trade-off solutions in comparison to the classical routing protocols.

关键词： Wireless Sensor Network multi-objective evolutionary algorithm SPEA2 Quality of Service

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evolutionary algorithms for multi-objective dual-resource constrained flexible job-shop scheduling problem

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OPSEARCH 2019年第3期56卷 983-1006页

作者： Yazdani, M. Zandieh, M. Tavakkoli-Moghaddam, R. Islamic Azad Univ Qazvin Branch Fac Ind & Mech Engn Dept Ind Engn Qazvin Iran Shahid Beheshti Univ GC Management & Accounting Fac Dept Ind Management Tehran Iran Univ Tehran Coll Engn Sch Ind Engn Tehran Iran

This paper presents a multi-objective dual-resource constrained flexible job-shop scheduling problem (MODRCFJSP) with the objectives of minimizing the makespan, critical machine workload and total workload of machines simultaneously. Two types of multi-objective evolutionary algorithms including fast elitist non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are proposed for solving MODRCFJSP. Some efficient mutation and crossover operators are adapted to the special chromosome structure of the problem for producing new solutions in the algorithm's generations. Besides, we provide controlled elitism based version of NSGA-II and NRGA, namely controlled elitist NSGA-II (CENSGA-II) and controlled elitist NRGA (CENRGA), to optimize MODRCFJSP. To show the performance of the four proposed algorithms, numerical experiments with randomly generated test problems are used. Moreover, different convergence and diversity performance metrics are employed to illustrate the relative performance of the presented algorithms.

关键词： Scheduling Flexible job-shop Dual-resource constrained multi-objective optimization multi-objective evolutionary algorithm Controlled elitism procedure

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