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Congress on evolutionary computation

作者： Noor H. Awad Mostafa Z. Ali Ponnuthurai N. Suganthan Robert G. Reynolds School of Electrical & Electronic Engineering Nanyang Technological University Singapore School of Computer Information Systems Jordan University of Science & Technology Jordan Jordan King Hussein Faculty of Computing Sciences Princess Sumayya University for Technology College of Engineering Wayne State University

An effective and efficient self-adaptation framework is proposed to improve the performance of the L-SHADE algorithm by providing successful alternative adaptation for the selection of control parameters. The proposed algorithm, namely LSHADE-EpSin, uses a new ensemble sinusoidal approach to automatically adapt the values of the scaling factor of the Differential Evolution algorithm. This ensemble approach consists of a mixture of two sinusoidal formulas: A non-Adaptive Sinusoidal Decreasing Adjustment and an adaptive History-based Sinusoidal Increasing Adjustment. The objective of this sinusoidal ensemble approach is to find an effective balance between the exploitation of the already found best solutions, and the exploration of non-visited regions. A local search method based on Gaussian Walks is used at later generations to increase the exploitation ability of LSHADE-EpSin. The proposed algorithm is tested on the IEEE CEC2014 problems used in the Special Session and Competitions on Real-Parameter Single Objective Optimization of the IEEE CEC2016. The results statistically affirm the efficiency and robustness of the proposed approach to obtain better results compared to L-SHADE algorithm and other state-of-the-art algorithms.

关键词： Sociology Statistics Algorithm design and analysis Indexes Optimization Benchmark testing evolutionary computation

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Automatic blended tone mapping through evolutionary optimization

Automatic blended tone mapping through evolutionary optimiza...

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Congress on evolutionary computation

作者： Xihe Gao Stephen Brooks Dirk V. Arnold Faculty of Computer Science Dalhousie University Halifax Nova Scotia Canada

Tone mapping is the process of transforming high dynamic range images for display on low dynamic range devices. While many tone mapping operators have been proposed, there is no single operator that generates optimal results under all conditions. Blending the results from multiple operators with varying weights allows for leveraging the strengths of each of the operators considered. Prior work has used interactive evolution as a tool for blended tone mapping. In this paper, we build on recent progress in the development of objective quality measures for tone mapped images that allows us to automate the process of evolving blended tone mapped images. The quality measure used assesses tone mapped images in terms of brightness, visual saliency, and detail reproduction in bright and dark regions and assigns an overall score to each image. The blended tone mapping problem can then be solved as an optimization problem, where the operators' parameters and the weights that determine the relative influence of each operator are tuned to generate images with optimal perceptual quality. We show that the optimization can be accomplished with an evolutionary algorithm. Experiments with high dynamic range images demonstrate the effectiveness of our approach.

关键词： Brightness Dynamic range Distortion Optimization evolutionary computation Image quality Quality assessment

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Estimation of easily visible position for unknown object grasping

Estimation of easily visible position for unknown object gra...

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Congress on evolutionary computation

作者： Hiroyuki Masuta Soichiro Nariki Tatsuo Motoyoshi Ken'ichi Koyanagi Toru Oshima Hun-ok Lim Dept. of Intelligent Systems Design Engineering Toyama Prefectural University Imizu Toyama Japan Dept. of Mechanical Engineering Kanagawa University Yokohama Kanagawa Japan

ISBN: (纸本)9781509006243

This paper describes the easily visible position for deciding action of unknown object grasping without all of predefined knowledge in a real situation. We have proposed a perception system which is composed of an online processable object detection method based on plane detection, and an invariant perceptual information for a grasping action. The object detection method can detect the point data on the object without the predefined knowledge. The invariant detection method for a grasping action is explained by inertia tensor and fuzzy inference. As an experiment, we compare the object extraction result and the invariant perceptual information value through experimentation of the various sensing position and the object pose. We discuss the relationship between the sensing position and the object detection result. Furthermore, we discuss the invariant perceptual information value which can be estimated the easily visible position of the object.

关键词： Handheld computers Decision support systems evolutionary computation Erbium Service robots Three-dimensional displays

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Cooperative co-evolution using LSHADE with restarts for the CEC15 benchmarks

Cooperative co-evolution using LSHADE with restarts for the ...

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Congress on evolutionary computation

作者： Mohammed El-Abd Electrical and Computer Engineering Department American University of Kuwait Kuwait

In this paper, we test the performance of an LSHADE Cooperative Co-evolutionary (CC) algorithm using the CEC15 benchmarks. First, we apply the recently proposed Global Differential Grouping (GDG) to learn the underlying interdependencies of the problem variables. GDG divides both separable and non-separable variables among multiple sets. Second, the method adopts the LSHADE algorithm within the CC framework to simultaneously optimize the identified groups. Results are reported for all required problem sizes.

关键词： Benchmark testing Context Convergence evolutionary computation Sociology Statistics Search problems

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CBCC3 — A contribution-based cooperative co-evolutionary algorithm with improved exploration/exploitation balance

CBCC3 — A contribution-based cooperative co-evolutionary al...

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Congress on evolutionary computation

作者： Mohammad Nabi Omidvar Borhan Kazimipour Xiaodong Li Xin Yao School of Computer Science University of Birmingham Birmingham U.K. School of Computer Science and Information Technology RMIT University Melbourne VIC Australia

ISBN: (纸本)9781509006243

Cooperative Co-evolution (CC) is a promising framework for solving large-scale optimization problems. However, the round-robin strategy of CC is not an efficient way of allocating the available computational resources to components of imbalanced functions. The imbalance problem happens when the components of a partially separable function have non-uniform contributions to the overall objective value. Contribution-Based Cooperative Co-evolution (CBCC) is a variant of CC that allocates the available computational resources to the individual components based on their contributions. CBCC variants (CBCC1 and CBCC2) have shown better performance than the standard CC in a variety of cases. In this paper, we show that over-exploration and over-exploitation are two major sources of performance loss in the existing CBCC variants. On that basis, we propose a new contribution-based algorithm that maintains a better balance between exploration and exploitation. The empirical results show that the new algorithm is superior to its predecessors as well as the standard CC.

关键词： Benchmark testing Optimization Linear programming Standards Resource management evolutionary computation Context

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A multi-modal optimization approach to single path planning for unmanned aerial vehicle

A multi-modal optimization approach to single path planning ...

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Congress on evolutionary computation

作者： Peng Yang Guanzhou Lu Ke Tang Xin Yao USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI) School of Computer Science and Technology University of Science and Technology of China Hefei China The Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA) School of Computer Science University of Birmingham Birmingham U.K.

In the past few years, evolutionary Algorithms (EAs) based UAV path planners have drawn increasing research interests. However, they are not scalable to large-scale problems, i.e., lots of waypoints. Recently, we have proposed a novel EA-based framework, named Separately Evolving Waypoints (SEW), that can deal with large-scale problems. However, the difficulty of UAV path planning depends not only on the number of waypoints, but on the number of constraints it has to satisfy, especially the number of obstacles. In particular, the number of waypoints required is also partly determined by the number of constraints. Hence, it is critical to further improve SEW with respect to large number of obstacles. Originally, a state-of-the-art global optimization approach is employed. In this work, we discuss how the increasing number of obstacles will deteriorate the performance of the global optimizer, then we propose multimodal optimization approaches that facilitates the performance of SEW against large number of obstacles.

关键词： Path planning Optimization Unmanned aerial vehicles Missiles Turning evolutionary computation Computer science

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Handling inverse optimal control problems using evolutionary bilevel optimization

Handling inverse optimal control problems using evolutionary...

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Congress on evolutionary computation

作者： Varun Suryan Ankur Sinha Pekka Malo Kalyanmoy Deb Department of Mechanical Engineering Indian Institute of Technology Jodhpur India Production and Quantitative Methods Indian Institute of Management Ahmedabad India Department of Information and Service Economy Aalto University School of Business Finland Computer Science and Engineering Michigan State University USA

Optimal control is a task where it is desired to determine the inputs of a dynamical system that optimize (minimize or maximize) a specified cost functional, also known as performance index, while satisfying any constraints on behaviour of the system. As the name suggests, inverse optimal control is the opposite of the former one and thus is associated with mining of the cost functional, optimal behaviour of which fits the given results best. In this paper, we present the importance of evolutionary bilevel optimization techniques as a promising approach to solve inverse optimal control problems. Generally, inverse optimal control problems are found to be ill posed which makes them computationally expensive in addition to the associated redundancy with the solution. Inverse optimal control theory works as a stepping stone in figuring out the underlying optimality criteria in a given task. It has several other applications in areas like Markov's Decision Processes and Game Theory. In our work, we solve inverse optimal control problems to retrieve the original functional in optimal control task using metaheuristic based bilevel optimization techniques. The dataset comprising of state variables generated from an optimal control problem is utilized to mine the functional. In the later part of our paper, we formulate a problem of human motion transfer as a bilevel optimization task, and subsequently solve it using a bilevel algorithm.

关键词： Optimal control Optimization Linear programming Inverse problems Electronic mail Performance analysis evolutionary computation

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Evolving polyomino puzzles

Evolving polyomino puzzles

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Congress on evolutionary computation

作者： Daniel Ashlock Lauren Taylor Department of Mathematics and Statistics at the University of Guelph

ISBN: (纸本)9781509006243

A polyomino puzzle is a collection of polyominos that can be joined to make a simple shape. The game Ten-Yen was one of the first of these. It has ten polyomino pieces that could be used to make a 6×6 square in a variety of ways. In this study we define representations and fitness functions for generating polyomino puzzles as well as developing a simple solver to compare the evolved puzzles. The solver can be used to approximate the number of solutions and hence the relative difficulty of the puzzles. Two types of fitness functions are compared, the second of which was developed to deal with scaling issues that arose with the first. A parameter study on the algorithm is performed and it is found that simply penalizing bad results is more effective than parameter tuning. This study concludes by discussing potential puzzle variants.

关键词： Shape evolutionary computation Sociology Statistics Games Color Biological cells

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The GA-based algorithms for optimizing hiding sensitive itemsets through transaction deletion

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APPLIED INTELLIGENCE 2015年第2期42卷 210-230页

作者： Lin, Chun-Wei Hong, Tzung-Pei Yang, Kuo-Tung Wang, Leon Shyue-Liang Harbin Inst Technol Shenzhen Grad Sch IIIRC Shenzhen Peoples R China Harbin Inst Technol Shenzhen Grad Sch Sch Comp Sci & Technol Shenzhen Key Lab Internet Informat Collaborat Shenzhen Peoples R China Natl Univ Kaohsiung Dept Comp Sci & Informat Engn Kaohsiung Taiwan Natl Sun Yat Sen Univ Dept Comp Sci & Engn Kaohsiung 80424 Taiwan Natl Univ Kaohsiung Dept Informat Management Kaohsiung Taiwan

Data mining technology is used to extract useful knowledge from very large datasets, but the process of data collection and data dissemination may result in an inherent threat to privacy. Some sensitive or private information concerning individuals, businesses and organizations has to be suppressed before it is shared or published. Privacy-preserving data mining (PPDM) has become an important issue in recent years. In the past, many heuristic approaches were developed to sanitize databases for the purpose of hiding sensitive information in PPDM, but data sanitization of PPDM is considered to be an NP-hard problem. It is critical to find the balance between privacy protection for hiding sensitive information and maintaining the discovery of knowledge, or even reducing artificial knowledge in the sanitization process. In this paper, a GA-based framework with two optimization algorithms is proposed for data sanitization. A novel evaluation function with three concerned factors is designed to find the appropriate transactions to be deleted in order to hide sensitive itemsets. Experiments are then conducted to evaluate the performance of the proposed GA-based algorithms with regard to different factors such as the execution time, the number of hiding failures, the number of missing itemsets, the number of artificial itemsets, and database dissimilarity.

关键词： Privacy preserving Data mining Genetic algorithm Pre-large concept evolutionary computation

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How to compare many-objective algorithms under different settings of population and archive sizes

How to compare many-objective algorithms under different set...

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Congress on evolutionary computation

作者： Hisao Ishibuchi Yu Setoguchi Hiroyuki Masuda Yusuke Nojima Department of Computer Science and Intelligent Systems Osaka Prefecture University Sakai Osaka Japan

ISBN: (纸本)9781509006243

In the evolutionary multi-objective optimization community, algorithm comparison is usually performed under the same population size. However, this is not always fair because its best specification is usually different in each algorithm. In many-objective optimization, the number of solutions to be found may depend on the situation. If the decision maker wants to analyze the entire Pareto front, thousands of solutions may be needed. If the decision maker wants to choose a single final solution from some candidates after their quick checks, only a small number of representative solutions may be needed. In this paper, we discuss how to evaluate the ability of evolutionary many-objective optimization algorithms to find an arbitrarily specified number of non-dominated solutions. Our idea is the use of solution selection after the termination of each algorithm. We examine two scenarios: One is solution selection from the final population, and the other is from all of the examined solutions. Through computational experiments, first we demonstrate that performance comparison heavily depends on the population size. Then we examine the effects of solution selection from the final population and the examined solutions on comparison results.

关键词： Sociology Statistics Optimization Algorithm design and analysis Approximation algorithms evolutionary computation computational modeling

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