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作者： Bari, Atm Golam University of South Florida

学位级别：Ph.D.

evolutionary algorithms (EA) have been successfully applied to a wide range of optimization and search problems where no mathematical model of the quality of a candidate solution is available. interactive evolutionary algorithms (IEA) and Competitive Coevolutionary algorithms (CCoEA) go one step further by being able to tackle problems where the only means to evaluate the quality of a candidate solution is via interactions. In a typical IEA, interactions take place between the solution being evolved and human evaluators. In a CCoEA, interactions take place between solutions themselves, without need for human interaction. This dissertation identifies computer-aided learning as an application domain that exemplifies the overlap of both fields. In particular, this work first develops a novel interactive and competitive (co)evolutionary approach to evolve candidate solutions. To do so, we identify viable algorithms, analyze them and author new variants of hill climber algorithms. Then, we design and implement a competitive coevolutionary interaction-based algorithm. The performance of the resulting heuristic is evaluated with respect to its ability to approximate a full Coevolutionary Dimension Extraction (CDE) process. This allows us to ensure that the proposed approach evolves candidate solutions that have pedagogically relevant in an educational application. However, the underlying hill climber algorithm produces some candidate solutions that exhibit the same interaction outcomes against opponent solutions. So, we also propose different approaches to improve the diversity of the solutions being evolved. To this end, we relax the strict acceptance condition in existing hill climbing algorithms relying on Pareto dominance. The proposed variant draw its inspiration from the Non-dominated Sorted Genetic algorithm (NSGA), commonly used in evolutionary multixobjectives optimization. We also introduce selection methods based on competitive shared fitness, and the analysis o

关键词： Concept Inventory interactive evolutionary algorithm Optimization Pareto Dominance Solution Concepts

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interactive Genetic algorithm with Implicit Uncertainty Evaluation for Application in Personalized Search

Interactive Genetic Algorithm with Implicit Uncertainty Eval...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Sun, Xiaoyan Chen, Yang Bao, Lin Xu, Ruidong China Univ Min & Technol Sch Informat & Control Engn Xuzhou Peoples R China China Univ Min & Technol Sch Elect & Power Engn Xuzhou Peoples R China

ISBN: (纸本)9781538627266

interactive evolutionary algorithms have been used successfully for search and optimization problems. However, user evaluation fatigue and uncertainties greatly restrict their application in complex optimization scenarios. Implicit evaluation techniques can reduce user fatigue effectively, but methods for handling evaluation uncertainties have not yet been explored thoroughly. We study an interactive evolutionary algorithm with uncertain preferences using an implicit evaluation model. First, we design a Gaussian function to define evaluation uncertainties based on interactions. Then, by combining the evaluation uncertainties and the interactions, we calculate the preference weights for each variable, with which a weighted Conditional Preference Net is constructed to provide quantitative depiction of the user's evaluation preference. Using the weighted Net, we estimate the fitness of an individual for the achievement of the evolutionary process. In the iteration, the Net is updated according to newly performed interactions to ensure reliable tracking of the user's preference. We apply a genetic algorithm-based interactive evolutionary algorithm to a personalized search for books. The proposed algorithm accelerates search speed, alleviates user fatigue, and achieves higher rates of satisfying solutions.

关键词： interactive evolutionary algorithm Conditional Preference Nets personalized search uncertainty

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interactive differential evolution for user-oriented image retrieval system

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SOFT COMPUTING 2016年第2期20卷 449-463页

作者： Yu, Fei Li, Yuanxiang Wei, Bo Kuang, Li Wuhan Univ State Key Lab Software Engn Wuhan 430072 Peoples R China East China Jiaotong Univ Sch Software Nanchang Peoples R China

Large amounts of image data have been produced on the Internet over the past several years. As a kind of effective retrieval way, the content-based image retrieval (CBIR) has attracted more and more attention. To improve the preciseness, most CBIR systems emphasize on finding the best representation for different image features. However, the semantic gap between visual description and user expectations is hard to handle. The relevance feedback technique can use relevance information to alleviate this problem. This paper describes a CBIR framework based on interactive differential evolution which uses a technique of combing the global and the local retrieval strategy to help users retrieve their preferred images in a user-oriented way. Experimental results show that the proposed framework increases the accuracy, and it outperforms the recent frameworks based on GAs.

关键词： interactive evolutionary algorithm Differential evolution Feedback relevance Content-based image retrieval

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interactive Genetic algorithm with Implicit Uncertainty Evaluation for Application in Personalized Search

Interactive Genetic Algorithm with Implicit Uncertainty Eval...

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IEEE Symposium Series on Computational Intelligence

作者： Xiaoyan Sun Yang Chen Lin Bao Ruidong Xu School of Information and Control Engineering China University of Mining and Technology Xuzhou China

关键词： interactive evolutionary algorithm Conditional Preference Nets Personalized search Uncertainty

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Dynamic Difficulty Adjustment in Games by Using an interactive Self-Organizing Architecture

Dynamic Difficulty Adjustment in Games by Using an Interacti...

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Iranian Conference on Intelligent Systems (ICIS)

作者： Ebrahimi, Adeleh Akbarzadeh-T., Mohammad-R. Islamic Azad Univ Dept Comp Engn Mashhad Branch Mashhad Iran Ferdowsi Univ Mashhad Ctr Excellence Soft Comp & Intelligent Informat P Iran Iran

ISBN: (纸本)9781479933501

If difficulty level of a game does not match player's skills, the game could be frustrating or disappointing. In this paper we propose a self-organizing system (SOS) to adjust difficulty level of games. For this purpose, we use Artificial Neural Network and interactive evolutionary algorithms to evolve Non-Player Characters (NPCs), and focus on player's hidden responses to determine fitness of the system. Results show that the proposed interactive SOS can adapt itself with different level of skills.

关键词： Self Organizng System interactive evolutionary algorithm CoOperative Coevolution Non-Player Character

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interactive evolutionary generation of facial composites for locating suspects in criminal investigations

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APPLIED SOFT COMPUTING 2013年第7期13卷 3298-3306页

作者： Solomon, Christopher J. Gibson, Stuart J. Mist, Joseph J. Univ Kent Sch Phys Sci Canterbury CT2 7NH Kent England

Statistical appearance models have previously been used for computer face recognition applications in which an image patch is synthesized and morphed to match a target face image using an automated iterative fitting algorithm. Here we describe an alternative use for appearance models, namely for producing facial composite images (sometimes referred to as E-FIT or PhotoFIT images). This application poses an interesting real-world optimization problem because the target face exists in the mind of the witness and not in a tangible form such as a digital image. To solve this problem we employ an interactive evolutionary algorithm that allows the witness to evolve a likeness to the target face. A system based on our approach, called EFIT-V, is used frequently by three quarters of UK police constabularies. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Facial composite interactive evolutionary algorithm Statistical appearance model Facial identification EFIT-V interactive evolution strategy

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Fireworks: evolutionary art project based on EvoSpace-interactive

Fireworks: Evolutionary art project based on EvoSpace-Intera...

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IEEE Congress on evolutionary Computation

作者： Trujillo, Leonardo Garcia-Valdez, Mario Fernandez-de-Vega, Francisco Merelo, Juan-J. Inst Tecnol Tijuana Div Estudios Posgrad Tijuana Mexico Univ Extremadura Grp Evoluc Artificial E-06071 Badajoz Spain Univ Granada Dept Arquitectura & Tecnol Computadores E-18071 Granada Spain

ISBN: (纸本)9781479904549;9781479904532

This paper presents a collaborative-interactive evolutionary algorithm (C-IEA) that evolves artistic animations and is executed on the web. The application is called Fireworks, since the animations that are produced are similar to an elaborate fireworks display. The system is built using the EvoSpace platform for distributed and asynchronous evolutionary algorithms. EvoSpace provides a central repository for the evolving population and remote clients, called EvoWorkers, that interact with the system to perform fitness evaluation using an interactive approach. The artistic animations are coded using the Processing programming language that facilitates rapid development of computer graphics applications for artists and graphic designers. The system promotes user collaboration and interaction by allowing many users to participate in population evaluation and because the system incorporates social networking. Initial results show that the proposed C-IEA can allow users to produce interesting artistic artifacts that incorporate preferences from several users, evolving dynamic animations that are unique within evolutionary art.

关键词： Distributed algorithms cloud computing interactive evolutionary algorithm linear genetic programming

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Accelerating Human-Computer Collaborative Search through Learning Comparative and Predictive User Models 12

Accelerating Human-Computer Collaborative Search through Lea...

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14th International Conference on Genetic and evolutionary Computation Conference (GECCO)

作者： Hornby, Gregory S. Bongard, Josh C. Univ Calif Santa Cruz NASA Ames Res Ctr Moffett Field CA USA

ISBN: (纸本)9781450311779

interactive evolutionary algorithms (IEAs) have much potential for allowing a human user to guide a search algorithm, but have struggled to overcome the limitations of slow, easily-fatigued human users. Here we describe The Approximate User (TAU) system in which these limitations are overcome by using a model of the user's preferences - which are continuously built and refined during the search process - to drive the search algorithm. Two variations of a user-modeling approach are compared to determine if this approach can accelerate IEA search. The two user-modeling approaches compared are: 1. learning a classifier which correctly determines which of two designs is better;and 2. learning a model which predicts a fitness score. Rather than having people do the user-testing, we propose the use of a simulated user as an easier means to test IEAs. Both variants of the TAU IEA are compared against a basic IEA and it is shown that TAU is up to 2.7 times faster and 15 times more reliable at producing near optimal results. In addition, we see TAU as a step toward building a more general Human-Computer Collaborative system.

关键词： evolutionary Design interactive evolutionary algorithm Preference Learning User Fatigue User Modeling

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EFIT-V -: interactive evolutionary strategy for the construction of photo-realistic facial composites 08

EFIT-V -: interactive evolutionary strategy for the construc...

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Proceedings of the 10th annual conference on Genetic and evolutionary computation

作者： Ben George Stuart J. Gibson Matthew I.S. Maylin Christopher J. Solomon University of Kent Canterbury United Kngdm

ISBN: (纸本)9781605581309

Facial composite systems are used to create a likeness to a suspect in criminal investigations. Traditional, feature-based facial composite systems rely on the witness' ability to recall individual features, provide verbal descriptions and then select them from stored libraries of labelled features - a task which witnesses often find difficult. The EFIT-V facial composite system is based on different principles, employing a holistic (whole face) approach to *** witness is shown a number of randomly generated faces and is asked to select the one that best resembles the target. A genetic algorithm is then used to breed a new generation of faces based upon the selected individual. This process is repeated until the user is satisfied with the composite *** paper describes the main components and methodology of EFIT-V and showcases the strengths of the system.

关键词： appearance models facial synthesis facial composites interactive evolutionary algorithm

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evolutionary Optimization of Hybrid Indices for Fashion Design

Evolutionary Optimization of Hybrid Indices for Fashion Desi...

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20th Chinese Control and Decision Conference

作者： Sun XiaoYan Gong DunWei China Univ Mining & Technol Sch Informat & Elect Engn Xuzhou 221008 Peoples R China

ISBN: (纸本)9781424417339

The color harmony and style novelty of fashion are both considered for fashion design problems. The characteristics of the problem are firstly analyzed. It drew the conclusion that this kind of problems contains explicit and implicit objectives simultaneously and the two objectives are not conflicting as typical multi-objective optimization problems. Secondly, the explicit expression of color harmony is presented and genetic algorithm is adopted to perform the optimization. Thirdly, interactive genetic algorithm is applied to optimize the style novelty and to obtain the evaluation of implicit index. The results obtained by the two methods respectively are integrated and the two indices are co-evolved. Lastly, the interactive interface of human-machine is given and the results compared with the optimization using merely interactive genetic algorithm show the validity of the presented algorithm.

关键词： Hybrid Performance Indices Genetic algorithm interactive evolutionary algorithm Fashion Design

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