Interactive Genetic Algorithm Research Articles

Elegant Object-Oriented Software Design via Interactive, Evolutionary Computation

Design is fundamental to software development but can be demanding to perform. Thus, to assist the software designer, evolutionary computing is being increasingly applied using machine-based, quantitative fitness functions to evolve software designs. However, in nature, elegance and symmetry play a crucial role in the reproductive fitness of various organisms. In addition, subjective evaluation has also been exploited in interactive evolutionary computation (IEC). Therefore, to investigate the role of elegance and symmetry in software design, four novel elegance measures are proposed which are based on the evenness of distribution of design elements. In controlled experiments in a dynamic IEC environment, designers are presented with visualizations of object-oriented software designs, which they rank according to a subjective assessment of elegance. For three out of the four elegance measures proposed, it is found that a significant correlation exists between elegance values and reward elicited. These three elegance measures assess the evenness of distribution of 1) attributes and methods among classes; 2) external couples between classes; and 3) the ratio of attributes to methods. It is concluded that symmetrical elegance is in some way significant in software design, and that this can be exploited in dynamic, multiobjective IEC to produce elegant software designs.

An interactive memetic algorithm for production and manufacturing problems modelled as a multi-objective travelling salesman problem

In this paper, a preference-based, interactive memetic random-key genetic algorithm (PIMRKGA) is developed and used to find (weakly) Pareto optimal solutions to manufacturing and production problems that can be modelled as a symmetric multi-objective travelling salesman problem. Since there are a large number of solutions to these kinds of problems, to reduce the computational effort and to provide more desirable and meaningful solutions to the decision maker, this research focuses on using interactive input from the user to explore the most desirable parts of the efficient frontier instead of trying to reproduce the entire frontier. Here, users define their preferences by selecting among five classes of objective functions and by specifying weighting coefficients, bounds, and optional upper bounds on indifference tradeoffs. This structure is married with the memetic algorithm – a random-key genetic algorithm hybridised by local search. The resulting methodology is an iterative process that continues until the decision maker is satisfied with the solution. The paper concludes with case studies utilising different scenarios to illustrate possible manufacturing and production related implementations of the methodology.

Optimized image resizing using flow-guided seam carving and an interactive genetic algorithm

In this paper, we introduce a novel method for content-aware image resizing based on flow-guided seam carving. It extends the existing seam carving framework by replacing the conventional energy field with a "structure-aware" energy field that takes into account the feature orientations in the image. Guided by this new energy field, our approach excels in preserving (i.e., avoiding the distortion of) important structures in the image, such as shape boundaries. We also present a simple user interface to further optimize the resizing result based on the genetic selection process among multiple resizing operators such as scaling, cropping, and flow-guided seam carving. We show that such simple user interaction, coupled with the genetic algorithm, dramatically increases the chances of producing the user-desired outcome.

Integrating Aesthetic and Usability Factors in the Design of Mobile Phones

Modern society has come to rely on mobile phones, devices of great functional value and aesthetic importance. Interactive Genetic Algorithms (IGAs) have previously been used to allow individuals to design mobile phones based on aesthetic preference. Here, a multi-objective ergonomic “teammate”, in the form of a Genetic Algorithm (GA) is added to the IGA. GAs and IGAs mimic evolution by iteratively refining a design by having users select designs they find most aesthetically pleasing (IGAs), or using a physical ergonomics model to select ergonomic designs (GAs), and using the selections to create a new generation of designs in a manner similar to natural selection. The web-based experiment had 32 participants, varying button spacing, screen size, and radius of phones over 10 trials (5 with an IGA alone, 5 with an IGA and a GA). The presence of a GA significantly improved the physical ergonomic rating of the final designs, but did not negatively impact the rated aesthetics. IGAs combined with GAs show promise as a tool for designers.

Comparison of Design Preferences for Mobile Phones and Blood Glucose Meters

Mobile Phones and Blood Glucose Meters are widely used personal digital input/output devices of great aesthetic and functional importance to their users. Interactive Genetic Algorithms (IGAs) are used to test for aesthetic preference links between these two devices. IGAs mimic natural selection by repeatedly having a human tester select designs they find the most aesthetically pleasing to serve as the parents of the next generation. The web-based IGA was used by 22 participants; varying button spacing, screen size, and radius of phones and glucose meters over 8 trials. Several links were found between the devices, specifically the screen-area to keypad-area ratio, and screen height to screen width ratio. IGAs show promise as a tool for designers to use a similar product to reduce the risk in a new product’s design by building on user’s current aesthetic experience.

Co-Evolutionary Model of Product Innovative Design Based on Kansei Engineering and Interactive Genetic Algorithms

Co-Evolutionary Model of Product Innovative Design Based on Kansei Engineering and Interactive Genetic Algorithms

Promoting Creative Design in Interactive Evolutionary Computation

We use a new measure of creativity as a guide in an interactive evolutionary art task and tie the results to natural language usage of the term “creative.” Following previous work, we explore a tractable definition of creativity, one emphasizing the novelty of systems, and its addition to an interactive application. We next introduce a generative ecosystemic art system, EvoEco, an agent-based pixel-level means of generating images. EvoEco is used as a component of an online survey which asks users to evolve a pleasing image and then rank the success of the process and its output. Evolutionary search is augmented with the creativity measure, and compared with control groups augmented with either random search or a measure of phenotypic distance. We show that users consistently rate the creativity measure-enhanced version as more “creative” and “novel” than other search techniques. We further derive additional insights into appropriate forms of genetic representation and pattern space-traversal in an interactive evolutionary algorithm.

On user-centric memetic algorithms

Memetic algorithms (MAs) constitute a metaheuristic optimization paradigm [usually based on the synergistic combination of an evolutionary algorithm (EA) and trajectory-based optimization techniques] that systematically exploits the knowledge about the problem being solved and that has shown its efficacy to solve many combinatorial optimization problems. However, when the search depends heavily on human-expert's intuition, the task of managing the problem knowledge might be really difficult or even indefinable/impossible; the so-called interactive evolutionary computation (IEC) helps to mitigate this problem by enabling the human user to interact with an EA during the optimization process. Interactive MAs can be constructed as reactive models in which the MA continuously demands the intervention of the human user; this approach has the drawback that provokes fatigue to the user. This paper considers user-centric MAs, a more global perspective of interactive MAs since it hints possibilities for the system to be proactive rather than merely interactive, i.e., to anticipate some of the user behavior and/or exhibit some degree of creativity, and provides some guidelines for the design of two different models for user-centric MAs, namely reactive and proactive search-based schema. An experimental study over two complex NP-hard problems, namely the Traveling Salesman problem and a Gene Ordering Problem, shows that user-centric MAs are in general effective optimization methods although the proactive approach provides additional advantages.

Interactive requirements prioritization using a genetic algorithm

ContextThe order in which requirements are implemented affects the delivery of value to the end-user, but it also depends on technical constraints and resource availability. The outcome of requirements prioritization is a total ordering of requirements that best accommodates the various kinds of constraints and priorities. During requirements prioritization, some decisions on the relative importance of requirements or the feasibility of a given implementation order must necessarily resort to a human (e.g., the requirements analyst), possessing the involved knowledge. ObjectiveIn this paper, we propose an Interactive Genetic Algorithm (IGA) that includes incremental knowledge acquisition and combines it with the existing constraints, such as dependencies and priorities. We also assess the performance of the proposed algorithm. MethodThe validation of IGA was conducted on a real case study, by comparing the proposed algorithm with the state of the art, interactive prioritization technique Incomplete Analytic Hierarchy Process (IAHP). ResultsThe proposed method outperforms IAHP in terms of effectiveness, efficiency and robustness to decision maker errors. ConclusionIGA produces a good approximation of the reference requirements ranking, requiring an acceptable manual effort and tolerating a reasonable human error rate.

Tournament Evaluation System Applying Win-Lose Result Presumption Considering Kansei Evaluation by Multiple People

We describe an Interactive Genetic Algorithm (IGA) with tournament evaluation for applying win-lose results obtained from an evaluation using multiple people. In our previous study, we developed an IGA with tournament evaluation as a basic model for evaluating candidate solutions using votes from multiple people [13]. However, tournament evaluation requires that IGA users evaluate the same candidate solution multiple times. Therefore, our previous method can reduce a user’s motivation for evaluating the solution. In addition, the number of users participating in a vote may decrease because of the decreasedmotivation to evaluate. To overcome this difficulty, we propose the application of a win-lose result presumption based on the tournament evaluation records of multiple people. When a system-based presumption is possible, the win-lose result presumption automatically determines the preferred and non-preferred candidates in each round. This method can reduce the number of times that users need to evaluate the same candidate solution. The effectiveness of the proposed method is verified using a numerical simulation that employs multiple numerical evaluation agents instead of human evaluators. The simulation results show an initial convergent improvement with the proposed method.

Interactive genetic algorithms with large population and semi-supervised learning

Interactive genetic algorithms are effective methods of solving optimization problems with implicit (qualitative) criteria by incorporating a user's intelligent evaluation into traditional evolution mechanisms. The heavy evaluation burden of the user, however, is crucial and limits their applications in complex optimization problems. We focus on reducing the evaluation burden by presenting a semi-supervised learning assisted interactive genetic algorithm with large population. In this algorithm, a population with many individuals is adopted to efficiently explore the search space. A surrogate model built with an improved semi-supervised learning method is employed to evaluate a part of individuals instead of the user to alleviate his/her burden in evaluation. Incorporated with the principles of the improved semi-supervised learning, the opportunities of applying and updating the surrogate model are determined by its confidence degree in estimation, and the informative individuals reevaluated by the user are selected according to the concept of learning from mistakes. We quantitatively analyze the performance of the proposed algorithm and apply it to the design of sunglasses lenses, a representative optimization problem with one qualitative criterion. The empirical results demonstrate the strength of our algorithm in searching for satisfactory solutions and easing the evaluation burden of the user.

Content based Image Retrieval using Implicit and Explicit Feedback with Interactive Genetic Algorithm

There are various new applications of genetic algorithms to information retrieval, mostly with respect to relevance feedback. However, they are yet to be evaluated in a way that allows them to be compared with each other and with other relevance feedback techniques. There is always need to efficiently store and retrieve image data to perform assigned tasks and to make a decision. This paper presents a new image retrieval framework with two types of relevance feedback i.e., implicit feedback in combination with explicit feedback. This paper employs Interactive Genetic Approach to discover a combination of descriptors that better characterizes the user perception of image resemblance. This approach provides better management and retrieval of images than the keyword-based approach. However, most of the conventional methods do not have the capability to effectively incorporate human interaction and emotion into retrieving images. In order to solve this problem we have developed an image retrieval system based on human preference and emotion by using an interactive genetic algorithm (IGA). In this approach we used two tier architecture of implicit and explicit feedback with IGA. Therefore, this system facilitates the search for the image not only with explicit queries, but also with implicit queries.

An interactive evolutionary approach to designing novel recommender systems

Recently, recommender systems have been widely used in e-commerce websites to help customers discover items they want. Since a recommender system should be able to provide users with helpful information regarding items that might interest them, the ability to immediately respond to changes in a user’s preference is a valuable asset of the systems. Thus, this work presents a novel recommender system that effectively adapts and immediately responds to any changes in the system by utilizing content-based filtering within the framework of interactive evolutionary computation. In addition, a data grouping technique is employed to enhance the computational time efficiency. The proposed system is then tested with music data. Experimental results confirm that the proposed system is able to offer users suitable music items with assured quality in a timely manner. Furthermore, the experimental results suggest that this system provides more reliable music recommendations than other content-based filtering systems even in the working environment changes. Key words: Recommender system, information filtering, interactive evolutionary computation.

A consultation and simulation system for product color planning based on interactive genetic algorithms

In the early stage of a design process, it is important to create numerous and varied possible color plans for the target consumer group. These color plans help individual designers quickly find a few good color design schemes and give the design team ideas for brainstorming. The color plan of a product consists of the color combinations of its components and decorative patterns, which strongly influence the feelings of customers and thus their desire to purchase. However, very few studies have discussed these issues. In this study, a consultation and simulation system for product color planning that helps designers obtain the optimal color planning for components and decorative patterns of a product is proposed. This system includes two parts: one uses the interactive genetic algorithm to establish a creative evolutionary system that can interact with a designer to explore novel design schemes; the other extends the boundary extract algorithm to establish a color simulation system that can apply colors to the areas of product components and decorative patterns for color simulation. Finally, a case study of color planning for a motorcycle model is used to verify the feasibility of the proposed system. © 2012 Wiley Periodicals, Inc. Col Res Appl, 38, 375–390, 2013

Color Combination System of Signboards to Blend in with Landscape by Interactive Genetic Algorithm

Color Combination System of Signboards to Blend in with Landscape by Interactive Genetic Algorithm

Using evolutionary design to interactively sketch car silhouettes and stimulate designer's creativity

An Interactive Genetic Algorithm is proposed to progressively sketch the desired side-view of a car profile. It adopts a Fourier decomposition of a 2D profile as the genotype, and proposes a cross-over mechanism. In addition, a formula function of two genes' discrepancies is fitted to the perceived dissimilarity between two car profiles. This similarity index is intensively used, throughout a series of user tests, to highlight the added value of the IGA compared to a systematic car shape exploration, to prove its ability to create superior satisfactory designs and to stimulate designer's creativity. These tests have involved six designers with a design goal defined by a semantic attribute. The results reveal that if “friendly” is diversely interpreted in terms of car shapes, “sportive” denotes a very conventional representation which may be a limitation for shape renewal.

IGA-Based User Evaluation Driven Optimization Design of Forklift Styling

Considering the requirements for efficiency and precision in the forklift styling, the authors developed the forklift optimizing system for its styling based on the interactive genetic algorithms (IGA). The system includes parameter optimization, color optimization and part combination three modules. The parameter optimization module can find the user defined parameters of the forklift 3D models and generate new models. Color optimization module can group the model surfaces and render the groups with different color. New color plans are generated through random changing of the colors in each group. The parts combination module divides forklift into several parts and build lib for each part. The module can pick parts from the lib and assemble them into a whole forklift and demonstrate them. The thesis developed a protosystem on the Solidworks platform with VBA programming tools. Interactive genetic algorithms are applied to realize the three module’s function.

Protocol analysis of designers using an interactive evolutionary computation

This paper explores the problem-solving behavior of people in design activities through a protocol analysis of verbal reports on the interior work design process simulated by an interactive evolutionary computation (IEC). The protocol analysis method was used to explore the ways of thinking of the participants throughout the process. The analysis reveals that different parts of the interior scene have different effects on the evaluations, and people tend to use the same evaluation criteria continuously on several images. This kind of behavior is consistent with that of professional designers in past studies and is revealed applicable to non-professionals in the current research.

Modified Interactive Evolutionary Computing for Speed Control of an Electric DC Motor

DC motors are important components of most of the process control industries. PID controllers are extensively used in DC motors for speed as well as position control. Tuning of PID controller parameters is an iterative process and needs an optimization to achieve the desired performance. In this paper a modified form of Interactive Evolutionary Computing (IEC) is used as the tool for achieving optimization of PID controller parameters for the speed control of DC motor. Different error models are used for optimization and a comparison with Genetic Algorithm based approach is presented.

Interactive Evolutionary Computation for Analyzing Human Awareness Mechanisms

We discuss the importance of establishing awareness science and show the idea of using interactive evolutionary computation (IEC) as a tool for analyzing awareness mechanism and making awareness models. First, we describe the importance of human factors in computational intelligence and that IEC is one of approaches for the so-called humanized computational intelligence. Second, we show examples that IEC is used as an analysis tool for human science. As analyzing human awareness mechanism is in this kind of analyzing human characteristics and capabilities, IEC may be able to be used for this purpose. Based on this expectation, we express one idea for analyzing the awareness mechanism. This idea is to make an equivalent model of an IEC user using a learning model and find latent variables that connect inputs and outputs of the user model and that help to understand or explain the inputs-outputs relationship. Although there must be several definitions of awareness, this idea is based on one definition that awareness is to find out unknown variables that helps our understanding. If we establish a method for finding the latent variables automatically, we can realize an awareness model in computer.