Ergonomic Chair Design by Fusing Qualitative and Quantitative Criteria using Interactive Genetic Algorithms

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 multi-objective 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 multi-objective approaches show promising results in fitness convergence, design diversity and user satisfaction metrics

Comparing Evolutionary Operators, Search Spaces, and Evolutionary Algorithms in the Construction of Facial Composites

Facial composite construction is one of the most successful applications of interactive evolutionary computation. In spite of this, previous work in the area of composite construction has not investigated the algorithm design options in detail. We address this issue with four experiments. In the first experiment a sorting task is used to identify the 12 most salient dimensions of a 30-dimensional search space. In the second experiment the performances of two mutation and two recombination operators for interactive genetic algorithms are compared. In the third experiment three search spaces are compared: a 30-dimensional search space, a mathematically reduced 12-dimensional search space, and a 12-dimensional search space formed from the 12 most salient dimensions. Finally, we compare the performances of an interactive genetic algorithm to interactive differential evolution. Our results show that the facial composite construction process is remarkably robust to the choice of evolutionary operator(s), the dimensionality of the search space, and the choice of interactive evolutionary algorithm. We attribute this to the imprecise nature of human face perception and differences between the participants in how they interact with the algorithms. Povzetek: Kompozitna gradnja obrazov je ena izmed najbolj uspešnih aplikacij interaktivnega evolucijskega ra?cunanja. Kljub temu pa do zdaj na podro?cju kompozitne gradnje niso bile podrobno raziskane možnosti snovanja algoritma. To vprašanje smo obravnavali s štirimi poskusi. V prvem je uporabljeno sortiranje za identifikacijo 12 najbolj izstopajo?cih dimenzij 30-dimenzionalnega preiskovalnega prostora. V drugem primerjamo u?cinkovitost dveh mutacij in dveh rekombinacijskih operaterjev za interaktivni genetski algoritem. V tretjem primerjamo tri preiskovalne prostore: 30-dimenzionalni, matemati?cno reducirani 12-dimenzionalni in 12-dimenzionalni prostor sestavljen iz 12 najpomembnejših dimenzij. Na koncu smo primerjali uspešnost interaktivnega genetskega algoritma z interaktivno diferencialno evolucijo. Rezultati kažejo, da je proces kompozitne gradnje obrazov izredno robusten glede na izbiro evolucijskega operatorja(-ev), dimenzionalnost preiskovalnega prostora in izbiro interaktivnega evolucijskega algoritma. To pripisujemo nenatan?cni naravi percepcije in razlikam med interakcijami uporabnikov z algoritmom

VISUALIZATION OF GENETIC ALGORITHM BASED ON 2-D GRAPH TO ACCELERATE THE SEARCHING WITH HUMAN INTERVENTIONS.

The Genetic Algorithm is an area in the field of Artificial Intelligence that is founded on the principles of biological evolution. Visualization techniques help in understanding the searching behaviour of Genetic Algorithm. lt also makes possible the user interactions during the searching process. It is noted that active user intervention increases the acceleration of Genetic Algorithm towards an optimal solution. In proposed research work, the user is aided by a visualization based on the representation of multidimensional Genetic Algorithm data on 2-0 space. The aim of the proposed approach is to study the benefit of using visualization techniques to explorer Genetic Algorithm data based on gene values. The user participates in the search by proposing a new individual. This is difTerent from existing Interactive Genetic Algorithm in which selection and evaluation of solutions is done by the users. A tool termed as VIGA-20 (Visualization of Genetic Algorithm using 2-0 Graph) is implemented to accomplish this goal. This visual tool enables the display of the evolution of gene values from generation to generation to observing and analysing the behaviour of the search space with user interactions. Individuals for the next generation are selected by using the objective function. Hence, a novel humanmachine interaction is developed in the proposed approach. The efficiency of the proposed approach is evaluated by two benchmark functions. The analysis and comparison of VIGA-20 is based on convergence test against the results obtained from the Simple Genetic Algorithm. This comparison is based on the same parameters except for the interactions of the user. The application of proposed approach is the modelling the branching structures by deriving a rule from best solution of VIGA-20. The comparison of results is based on the different user's perceptions, their involvement in the VIGA-20 and the difference of the fitness convergence as compared to Simple 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

Interactive Evolutionary Algorithms for Image Enhancement and Creation

Image enhancement and creation, particularly for aesthetic purposes, are tasks for which the use of interactive evolutionary algorithms would seem to be well suited. Previous work has concentrated on the development of various aspects of the interactive evolutionary algorithms and their application to various image enhancement and creation problems. Robust evaluation of algorithmic design options in interactive evolutionary algorithms and the comparison of interactive evolutionary algorithms to alternative approaches to achieving the same goals is generally less well addressed. The work presented in this thesis is primarily concerned with different interactive evolutionary algorithms, search spaces, and operators for setting the input values required by image processing and image creation tasks. A secondary concern is determining when the use of the interactive evolutionary algorithm approach to image enhancement problems is warranted and how it compares with alternative approaches. Various interactive evolutionary algorithms were implemented and compared in a number of specifically devised experiments using tasks of varying complexity. A novel aspect of this thesis, with regards to other work in the study of interactive evolutionary algorithms, was that statistical analysis of the data gathered from the experiments was performed. This analysis demonstrated, contrary to popular assumption, that the choice of algorithm parameters, operators, search spaces, and even the underlying evolutionary algorithm has little effect on the quality of the resulting images or the time it takes to develop them. It was found that the interaction methods chosen when implementing the user interface of the interactive evolutionary algorithms had a greater influence on the performances of the algorithms

Mindfulness mirror

This paper explores the use of an interactive Genetic Algorithm for creating a piece of visual art intended to assist in promoting the state of mindfulness. This is determined by a Bluetooth gaming electroencephalography (EEG) headset as the fitness function. The visual display consisted of an infinity mirror with over two hundred Neopixels with fade times and colour of zones controlled by two Ardu-inos running the software. Whilst we have observed some convergence of solu-tions, the results and user observations raised some interesting questions about how this strategy might be improved

Diversity-based adaptive genetic algorithm for a workforce scheduling and routing problem

The Workforce Scheduling and Routing Problem refers to the assignment of personnel to visits across various geographical locations. Solving this problem demands tackling numerous scheduling and routing constraints while aiming to minimise total operational cost. One of the main obstacles in designing a genetic algorithm for this highly-constrained combinatorial optimisation problem is the amount of empirical tests required for parameter tuning. This paper presents a genetic algorithm that uses a diversity-based adaptive parameter control method. Experimental results show the effectiveness of this parameter control method to enhance the performance of the genetic algorithm. This study makes a contribution to research on adaptive evolutionary algorithms applied to real-world problems

Optimizing Website Design Through the Application of an Interactive Genetic Algorithm

The goal of this project was to determine the efficacy and practicality of “optimizing” the design of a webpage through the application of an interactive genetic algorithm. Software was created to display a “population” of mutable designs, collect user feedback as a measure of fitness, and apply genetic operations in an ongoing evolutionary process. By tracking the prevalence of design parameters over multiple generations and evaluating their associated “fitness” values, it was possible to judge the overall performance of the algorithm when applied to this unique problem space