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On distributed user-centric memetic algorithms

SOFT COMPUTING 2019年第12期23卷 4019-4039页

作者： Fernandez-Leiva, Antonio J. Gutierrez-Fuentes, Alvaro Univ Malaga ETSI Informat Dept Lenguajes & Ciencias Comp Campus Teatinos E-29071 Malaga Spain

A user-centric memetic algorithm (UcMA) represents an instance of the so-called interactive evolutionary computation, in which the subjacent algorithm that interacts with a human consists of a memetic algorithm (MA) that manages knowledge of the problem with the aim of accelerating the solution search process. UcMAs have been proved to be effective optimization methods to tackle problems that require human intervention (in the form of evaluations/decisions). This paper proposes two generic schemas to distribute a number of (interactive/proactive) UcMAs that act as independent agents and, eventually, synchronize to interchange information. These schemas can be instantiated via a number of parameters (including the cooperation topology) to develop novel 2-dimensional spatially structured cooperative UcMAs. These algorithms can be viewed as specific instances of the so-called parallel MAs but with the particularity that they are especially suited to dealing with combinatorial problems whose solving requires subjective evaluations. An experimental study over microarray ordering problems is done including distinct instances of an NP-hard problem with strong implications in biomedicine and molecular biology, namely the gene ordering problem. It is shown that some distributed UcMAs, especially those based on proactivity, which have been instantiated from our proposals, efficiently handle these problems.

关键词： Distributed computing Memetic algorithm interactive evolutionary computation User-centric optimization Combinatorial optimization Gene ordering problem

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Stigmergic dimensions of online creative interaction

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COGNITIVE SYSTEMS RESEARCH 2013年 21卷 65-74页

作者： Secretan, Jimmy Korrelate Inc Orlando FL 32839 USA

This paper examines the stigmergic dimensions of online interactive creativity through the lens of Picbreeder. Picbreeder is a web-based system for collaborative interactive evolution of images. The Picbreeder applet starts by randomly generating several images, which are then mated and mutated based on the user's selections. The user can then publish the image to the Picbreeder website where other users can download and continue the image's evolution. Within this process, users collaboratively create images with significant complexity, all without explicit communication. In short, Picbreeder encourages a new form of stigmergic collaborative creation. The most surprising result of the Picbreeder experiment during more than 3 years of operation has been the quality of the resulting images, despite the limited ways of interacting with other users. This fact challenges some commonly held notions of creativity, both online and offline. While current cognitive research in creativity places significant emphasis of the personal traits and cognitive structures that give rise to creative thought, Picbreeder highlights the potential for the emergence of creativity through stigmergic interaction. Picbreeder offers a rich data set for analysis of collaborative interaction with over 155,000 inputs from hundreds of users combined to create over 7500 images. It is hoped that the insights offered in this paper will influence both the understanding of collaborative creativity and the development of new modes of online creative interaction. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Stigmergy Creativity interactive evolutionary computation Collaborative interactive evolution Memetic evolution

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Ergonomic chair design by fusing qualitative and quantitative criteria using interactive genetic algorithms

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IEEE TRANSACTIONS ON evolutionary computation 2008年第3期12卷 343-354页

作者： Brintrup, Alexandra Melike Ramsden, Jeremy Takagi, Hideyuki Tiwari, Ashutosh Univ Cambridge Dept Engn Cambridge CB2 1RX England Cranfield Univ Sch Appl Sci Cranfield MK43 0AL Beds England Kyushu Univ Fac Design Fukuoka 8158540 Japan

This paper emphasizes the necessity of formally bringing qualitative and quantitative criteria of ergonomic design together, and provides a novel complementary design framework with this aim. Within this framework, different design criteria are viewed as optimization objectives, and design solutions are iteratively improved through the cooperative efforts of computer and user. The framework is rooted in multiobjective optimization, genetic algorithms, and interactive user evaluation. Three different algorithms based on the framework are developed, and tested with an ergonomic chair design problem. The parallel and multiobjective approaches show promising results in fitness convergence, design diversity, and user satisfaction metrics.

关键词： design optimization ergonomics interactive evolutionary computation multiobjective optimization.

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Character Design Generation System Using Multiple Users' Gaze Information

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2021年第9期E104D卷 1459-1466页

作者： Takenouchi, Hiroshi Tokumaru, Masataka Fukuoka Inst Technol Fukuoka 8110295 Japan Kansai Univ Suita Osaka 5648680 Japan

We investigate an interactive evolutionary computation (IEC) using multiple users' gaze information when users partially participate in each design evaluation. Many previous IEC systems have a problem that user evaluation loads are too large. Hence, we proposed to employ user gaze information for evaluating designs generated by IEC systems in order to solve this problem. In this proposed system, users just view the presented designs, not assess, then the system automatically creates users' favorite designs. With the user's gaze information, the proposed system generates coordination that can satisfy many users. In our previous study, we verified the effectiveness of the proposed system from a real system operation viewpoint. However, we did not consider the fluctuation of the users during a solution candidate evaluation. In the actual operation of the proposed system, users may change during the process due to the user interchange. Therefore, in this study, we verify the effectiveness of the proposed system when varying the users participating in each evaluation for each generation. In the experiment, we employ two types of situations as assumed in real environments. The first situation changes the number of users evaluating the designs for each generation. The second situation employs various users from the predefined population to evaluate the designs for each generation. From the experimental results in the first situation, we confirm that, despite the change in the number of users during the solution candidate evaluation, the proposed system can generate coordination to satisfy many users. Also, from the results in the second situation, we verify that the proposed system can also generate coordination which both users who participate in the coordination evaluation can more satisfy.

关键词： interactive evolutionary computation gaze information multiple users partial evaluation character model

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An Efficiency of interactive Differential Evolution for Optimization of Warning Sound with Reflecting Individual Preference

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2015年第Sup1期10卷 S77-S82页

作者： Fukumoto, Makoto Fukuoka Inst Technol Fac Informat Engn Higashi Ku 3-30-1 Wajirohigashi Fukuoka 8110295 Japan

interactive evolutionary computation (IEC) is a method for creating media contents suited to the user's preference. Efficiency of previous IEC studies was generally investigated from the view point of optimization. However, few previous studies paid attention to their efficiency as a reflection of individual preferences. As an application of IEC, we already proposed a method that creates sign sounds using paired comparison-based interactive differential evolution (IDE). This study investigates the efficiency of the IDE for reflecting individual difference on the created sound through two experiments. In Experiment 1, the subjects searched better warning sounds with the IDE. The result of Experiment 1 showed gradual convergence of DE's vectors. In Experiment 2, the subjects evaluated four sign sounds created in Experiment 1. Two of them were created by the subject, and the other two were created by another subject. The result of Experiment 2 showed significant increase in the subjective fitness value for sounds created by the subjects themselves. Furthermore, significant difference between sounds by one subject and another was observed in the final generation. These results suggest that IDE is effective in creating warning sounds suited to each user reflecting individual difference. (C) 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： interactive evolutionary computation differential evolution individual preference sign sound

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Facility layout design using a multi-objective interactive genetic algorithm to support the DM

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EXPERT SYSTEMS 2015年第1期32卷 94-107页

作者： Garcia-Hernandez, Laura Arauzo-Azofra, Antonio Salas-Morera, Lorenzo Pierreval, Henri Corchado, Emilio Univ Cordoba Area Project Engn E-14071 Cordoba Spain LIMOS UMR CNRS 6158 IFMA Clermont Ferrand France Univ Salamanca Dept Informat & Automat E-37008 Salamanca Spain

The unequal area facility layout problem (UA-FLP) has been addressed by many methods. Most of them only take aspects that can be quantified into account. This contribution presents a novel approach, which considers both quantitative aspects and subjective features. To this end, a multi-objective interactive genetic algorithm is proposed with the aim of allowing interaction between the algorithm and the human expert designer, normally called the decision maker (DM) in the field of UA-FLP. The contribution of the DM's knowledge into the approach guides the complex search process, adjusting it to the DM's preferences. The entire population associated to facility layout designs is evaluated by quantitative criteria in combination with an assessment prepared by the DM, who gives a subjective evaluation for a set of representative individuals of the population in each iteration. In order to choose these individuals, a soft computing clustering method is used. Two interesting real-world data sets are analysed to empirically probe the robustness of these models. The first UA-FLP case study describes an ovine slaughterhouse plant and the second, a design for recycling carton plant. Relevant results are obtained, and interesting conclusions are drawn from the application of this novel intelligent framework.

关键词： multi-objective unequal area facility layout problem interactive evolutionary computation facility layout

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Integrated Approach to Personalized Procedural Map Generation Using evolutionary Algorithms

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IEEE TRANSACTIONS ON computationAL INTELLIGENCE AND AI IN GAMES 2015年第2期7卷 139-155页

作者： Raffe, William L. Zambetta, Fabio Li, Xiaodong Stanley, Kenneth O. RMIT Univ Sch Comp Sci & IT Melbourne Vic 3001 Australia Univ Cent Florida Dept Elect Engn & Comp Sci Orlando FL 32816 USA

In this paper, we propose the strategy of integrating multiple evolutionary processes for personalized procedural content generation (PCG). In this vein, we provide a concrete solution that personalizes game maps in a top-down action-shooter game to suit an individual player's preferences. The need for personalized PCG is steadily growing as the player market diversifies, making it more difficult to design a game that will accommodate a broad range of preferences and skills. In the solution presented here, the geometry of the map and the density of content within that geometry are represented and generated in distinct evolutionary processes, with the player's preferences being captured and utilized through a combination of interactive evolution and a player model formulated as a recommender system. All these components were implemented into a test bed game and experimented on through an unsupervised public experiment. The solution is examined against a plausible random baseline that is comparable to random map generators that have been implemented by independent game developers. Results indicate that the system as a whole is receiving better ratings, that the geometry and content evolutionary processes are exploring more of the solution space, and that the mean prediction accuracy of the player preference models is equivalent to that of existing recommender system literature. Furthermore, we discuss how each of the individual solutions can be used with other game genres and content types.

关键词： Hierarchical optimization interactive evolutionary computation neuroevolution of augmenting topologies personalized game maps procedural content generation recommender systems

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Learning aesthetic judgements in evolutionary art systems

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GENETIC PROGRAMMING AND EVOLVABLE MACHINES 2013年第3期14卷 315-337页

作者： Li, Yang Hu, Changjun Minku, Leandro L. Zuo, Haolei Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Beijing Key Lab Knowledge Engn Mat Sci Beijing 100083 Peoples R China Univ Birmingham Sch Comp Sci CERCIA Birmingham B15 2TT W Midlands England

Learning aesthetic judgements is essential for reducing users' fatigue in evolutionary art systems. Although judging beauty is a highly subjective task, we consider that certain features are important to please users. In this paper, we introduce an adaptive model to learn aesthetic judgements in the task of interactive evolutionary art. Following previous work, we explore a collection of aesthetic measurements based on aesthetic principles. We then reduce them to a relevant subset by feature selection, and build the model by learning the features extracted from previous interactions. To apply a more accurate model, multi-layer perceptron and C4.5 decision tree classifiers are compared. In order to test the efficacy of the approach, an evolutionary art system is built by adopting this model, which analyzes the user's aesthetic judgements and approximates their implicit aesthetic intentions in the subsequent generations. We first tested these aesthetic measurements on different artworks from our selected artists. Then, a series of experiments were performed by a group of users to validate the adaptive learning model. The study reveals that different features are useful for identifying different patterns, but not all are relevant for the description of artists' styles. Our results show that the use of the learning model in evolutionary art systems is sound and promising for predicting users' preferences.

关键词： evolutionary art interactive evolutionary computation Image complexity Fractal compression

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Automatic Construction of Loading Algorithms With interactive Genetic Programming

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IEEE ACCESS 2022年 10卷 125167-125180页

作者： Hiruta, Yusuke Nishihara, Kei Koguma, Yuji Fujii, Masakazu Nakata, Masaya Yokohama Natl Univ Grad Sch Engn Sci Yokohama Kanagawa 2408501 Japan IHI Corp Yokohama Kanagawa 2358501 Japan Yokohama Natl Univ Fac Environm & Informat Sci Yokohama Kanagawa 2408501 Japan Yokohama Natl Univ Fac Engn Yokohama Kanagawa 2408501 Japan

The design of a freight loading pattern is often conducted by skilled workers, who handle unquantifiable objectives and/or preferences. Our previous study presented an automatic construction technique for loading algorithms using genetic programming-based hyper-heuristics;however, this technique is only applicable to fully quantifiable loading problems. Thus, the approach described in this paper integrates an interactive framework with users into our previous technique to automatically construct algorithms that derive loading patterns adapted to user objectives and/or preferences. Thus, once a loading algorithm has been derived with user interactions, it can be reused to obtain the preferred loading patterns on other problems without any additional interactions. Experimental results show that the proposed algorithm can produce loading algorithms adapted for user preferences under a limit of 50 human interactions. Further, we also show that the derived loading algorithms can be applicable to different loading situations without any additional user interactions. Thus, these observations suggest the benefit of our approaches in reducing the burden placed on skilled workers for practical LPD tasks.

关键词： Genetic programming automatic algorithm construction interactive evolutionary computation loading optimization problem

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A Drug Candidate Design Environment Using evolutionary computation

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IEEE TRANSACTIONS ON evolutionary computation 2008年第5期12卷 591-603页

作者： Ecemis, M. Ihsan Wikel, James Bingham, Christopher Bonabeau, Eric Coalesix Inc Cambridge MA 02138 USA Icosystem Inc Cambridge MA 02138 USA

This paper describes the Candidate Design Environment we developed for efficient identification of promising drug candidates. Developing effective drugs from active molecules is a challenging problem which requires the simultaneous satisfaction of many factors. Traditionally, the drug discovery process is conducted by medicinal chemists whose vital expertise is not readily quantifiable. Recently, in silico modeling and virtual screening have been emerging as valuable tools despite their mixed results early on. Our approach combines the capabilities of computational models with human knowledge using a genetic algorithm and interactive evolutionary computation. We enable the chemist's expertise to play a key role in every stage of the discovery process. Our evolved structures are guaranteed to be within the chemistry space specified by the medicinal chemist, thereby making the results plausible. In this paper, we describe our approach, introduce a case study to test our methodology, and present our results.

关键词： computational chemistry drug design drug discovery genetic algorithms (GAs) in silico modeling interactive evolutionary computation lead optimization (LO) virtual screening

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