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In information visualisation, the visual artefacts should have a functional dimension, allowing the analysis of information, and an aesthetic dimension, to seize the users' attention to the information being displayed. However, in the data aesthetics field, the main concern is to produce aesthetically appealing artefacts. With this project, our goal is to try to join these two fields by exploring the aesthetic dimension of a functional visualisation model characterised by a series of parameters that can make the visualisation more functional and/or more aesthetically appealing. In concrete, we propose a framework based on interactive evolutionary computation (IEC) to evolve the parameters of the visualisation model, enabling the user to explore possibilities and to create different aesthetics over the same data. Our case study will be based on a dataset containing the consumption patterns within a Portuguese retail company. Through different validation methods - automatic fitness function, usage scenarios, and a user study - we show that our system is able to create a wide and diverse set of emergent visual artefacts that can be intriguing and aesthetically appealing for the user.

Natural evolution has produced a tremendous diversity of functional organisms. Many believe an essential component of this process was the evolution of evolvability, whereby evolution speeds up its ability to innovate by generating a more adaptive pool of offspring. One hypothesized mechanism for evolvability is developmental canalization, wherein certain dimensions of variation become more likely to be traversed and others are prevented from being explored (e.g. offspring tend to have similarly sized legs, and mutations affect the length of both legs, not each leg individually). While ubiquitous in nature, canalization almost never evolves in computational simulations of evolution. Not only does that deprive us of in silico models in which to study the evolution of evolvability, but it also raises the question of which conditions give rise to this form of evolvability. Answering this question would shed light on why such evolvability emerged naturally and could accelerate engineering efforts to harness evolution to solve important engineering challenges. In this paper we reveal a unique system in which canalization did emerge in computational evolution. We document that genomes entrench certain dimensions of variation that were frequently explored during their evolutionary history. The genetic representation of these organisms also evolved to be highly modular and hierarchical, and we show that these organizational properties correlate with increased fitness. Interestingly, the type of computational evolutionary experiment that produced this evolvability was very different from traditional digital evolution in that there was no objective, suggesting that open-ended, divergent evolutionary processes may be necessary for the evolution of evolvability., SI can be found at: http://www.evolvingai.org/files/SI_0.zip

In recent years, the possibility of using recurrent neural networks (RNNs) to model long-term dependencies in music and to generate novel pieces of music has been actively investigated. However, previous work on music generation by RNNs has simply generated music autonomously without user control. Thereby, previous studies have not been effective in assisting human composers. To overcome this, we incorporated the method of interactive evolutionary computation (IEC) in order to search through the latent space of music modeled by recurrent variational auto-encoders. By using IEC, we were able to integrate human perception into the system and enable the user to search for their desired type of music by interacting with the system. The proposed method effectively combines the impressive sequential data modeling capabilities of RNNs with the interactivity and user-friendliness of IEC-based systems. We conducted a user study to evaluate the usefulness of our method in comparison to conventional composition methods. The results revealed that our proposed method was effective in aiding human composition.

In this chapter, we propose a new interactive evolutionary computation (IEC) algorithm, we call it interactive chaotic evolution (ICE), which fuses the optimization capability of chaotic evolution and the subjective evaluation of human. We make a brief review on the research of IEC optimization and present the philosophy and the implementation of chaotic evolution and some chaotic systems that can be involved in ICE optimization framework. For a comparison study of ICE, we introduce the interactive differential evolution algorithm (IDE) as a comparative algorithm. We investigate the optimization performance of ICE using some benchmark functions as pseudo-IEC evaluators, and several statistical tests are applied. We analyse and discuss the subjects on paired comparison mechanism of ICE, the distribution characteristic of chaotic system and optimization capability of ICE, the characteristics of combining a chaotic system and a uniform random system, and fusion of ICE and IDE. In this work, we do not only pursue to analyse and discuss the algorithm optimization mechanism of ICE but also induce the philosophy and methodology behind it. We hope that many people realize that the capability of ICE is not only optimization from these efforts, and ICE can make benefit to both chaos theory and evolutionary optimization, perspectively.

This paper proposes a cooperative optimization method between a system and a user for problems involving quantitative and qualitative optimization criteria. In general Interactive Evolutionary Computation (IEC) models, a system and a user have their own role of evolution, such as individual reproduction and evaluation. In contrast, the proposed method allows them to dynamically switch their roles during the search by using explicit fitness function and case-based user preference prediction. For instance, in the proposed method, the system performs a global search at the beginning, the user then intensifies the search area, and finally the system conducts a local search at the intensified search area. This paper applies the proposed method for an image processing filter design problem that involves both quantitative (filter output accuracy) and qualitative criterion (filter behavior). Experiments have shown that the proposed cooperation method could design filters that are in accordance with user preference and have better performance than filters obtained by Non-IEC search.

We overview three research directions of interactive evolutionary computation (IEC). They are (1) extending IEC applications, (2) reducing IEC user fatigue by improving/ developing interface, algorithms, operators, frameworks, and others, and (3) using IEC as a tool for analyzing human characteristics.

A key issue in software evolution analysis is being able to compute evolutionary change accurately and with rich semantics. This dissertation describes a mathematical framework for enabling accurate computation of semantic evolutionary change. It is based on graphs for representing software semantics, graph transformations for modeling evolution, and effects of graph transformations for capturing evolutionary change. We formulate the notions of evolution sets and the evolution distance to measure evolutionary change. Then, we define an appropriate notion of optimal graph alignment to compute evolutionary change accurately. Establishing a rigorous foundation for computing evolutionary change is important for developing powerful automated tools for software evolution analysis. Cost estimation, software merging, reliability analysis, clone detection, incremental testing, validation and other software applications can benefit from precise computation of evolutionary change. A rigorous foundation also allows leveraging the extensive research on graph alignments to advance software engineering. We have created a framework for experimental evaluation of graph alignment algorithms. The framework includes a graph testbed, an accuracy metric, and a graph alignment visualization (GAV) mechanism. The framework is targeted at applications where a precise computation of evolutionary change from one system to the next is needed to reveal valuable knowledge about the system and its evolution. The accuracy metric is based on a new measure for graph difference and a new notion of optimality of graph alignment. The metric is designed to measure the degree to which an alignment is inaccurate, that is the degree to which it reports spurious differences. Such a metric is meaningful for estimating the efficiency of and resources necessary for many software evolution analyses.

Interactive Evolution (ie) has shown promise in domains where objective-driven approaches are problematic. Collaborating by branching from the evolutionary stepping stones discovered by others (cie) has allowed users to evolve a large variety of artefacts. In this paper we explore the potential of collaboratively evolving behaviours in Minecraft through an online cie system. Results suggest that it is indeed possible to solve a maze navigation task in Minecraft collaboratively, which is difficult to achieve individually. We hope that the released framework will allow others to build more complex domains, solving a series of more and more challenging tasks through many collaborating humans.

Interactive Evolutionary Computation (IEC) is used for creating media contents suited to each user's feelings. To obtain media contents commonly suited to multiple users, distributed approach was employed in previous IECs based on Genetic Algorithm. This study proposes Distributed Interactive Differential Evolution (DIDE) by employing Differential Evolution (DE). Based on each user's subjective selection and scoring with Interactive DE, DIDE exchanges good solutions between users. Through the exchange, it is expected that the good solution for all users is obtained. A fundamental experiment was conducted with a simple DIDE system creating sign sounds for two users. Distance between DE vectors obtained by two users and fitness values were investigated.

The fertility of two structures in an evolutionary computation system is the expected fitness of their potential offspring. The study uses the problem of locating interesting fractals to introduce an application of fertility intended to reduce user fatigue in Interactive Evolutionary Computation with a human-in-the-loop evaluation method. High fertility sets of fractal parameters are shown to substantially increase the performance of evolution using small population size, a surrogate for human-driven selection. The high fertility sets of fractal parameters are a form of automatically generated content that is part of an application intended to permit a user to find pleasing fractals.

This paper presents an interactive texture chooser. This interactive texture chooser allows novice users to obtain their desired textures by checking each 3D model that mapped the texture. Current 3D computer graphics (3DCG) software products provide a convenient means of dealing with 3D content for novice users. Textures are frequently used in several 3DCG tools. Users may use certain tag and categorical search interfaces for texture choices when they decide on each texture for a 3D mapping task. However, making textures choices can be challenging for novices because they have no sufficient knowledge about 3DCG. There are various types of textures even if several textures are classified in the same category. For example, searching for the desired stone texture can be challenging from the stone texture category because hundreds of textures have the same tag and are classified under the same category. In addition, the tag and categorical search interfaces are no longer available when a user does not have a specific target in his/her mind. The proposed interactive texture chooser solves the above issues. This system consists of an interactive evolutionary computation (IEC) mode and a similarity search (SS) mode. The IEC mode displays some texture candidates using an evolutionary computation algorithm. The SS mode consists of a visual similarity search function based on several texture features. The effectiveness of this system has been confirmed through experimental results.

In this study, we examine the effectiveness of a female-clothing coordination-generation system using eye-tracking information for multiple users. In our previous study, only two subjects used an interactive evolutionary computation (IEC) system by utilizing the users eye-tracking information, which however poses two issues for actual application. First, the number of subjects simultaneously using the system was low. Moreover, they were given specific instructions on how to use the system in advance. To solve these issues, we review our previous method, and develop a female-clothing coordination-generation system using digital signage and verify its effectiveness. The experimental results demonstrate that the system can simultaneously create satisfactory clothing coordination for multiple users.

This paper describes an approach that combines generative adversarial networks (GANs) with interactive evolutionary computation (IEC). While GANs can be trained to produce lifelike images, they are normally sampled randomly from the learned distribution, providing limited control over the resulting output. On the other hand, interactive evolution has shown promise in creating various artifacts such as images, music and 3D objects, but traditionally relies on a hand-designed evolvable representation of the target domain. The main insight in this paper is that a GAN trained on a specific target domain can act as a compact and robust genotype-to-phenotype mapping (i.e. most produced phenotypes do resemble valid domain artifacts). Once such a GAN is trained, the latent vector given as input to the GAN’s generator network can be put under evolutionary control, allowing controllable and high-quality image generation. In this paper, we demonstrate the advantage of this novel approach through a user study in which participants were able to evolve images that strongly resemble specific target images.

We propose a catalog interface for the Interactive Evolutionary Computation (IEC) system. When a user views a product catalog, the system obtains the user’s gaze information and implements evolutionary computation. We verify the effectiveness of the proposed system using evaluation experiments with real users. The experimental results show that the proposed system can generate solutions that offer results equivalent to comparable systems and reduce the evaluation load of the users.

We describe an aesthetic learning approach to one of the most challenging problems in interactive evolutionary design: modeling user preference for lessening the burden placed on users in hundreds of loops. In the approach, two aesthetic descriptors, high-level and low-level descriptors, are proposed based on pixel distribution and aesthetic criteria respectively. Starting with a collection of evaluated images, we apply both descriptors to the images, and then use decision tree learning algorithm to obtain the computational learning model. The model is adopted to automatically guide the subsequent generations. Classification and evolutionary results are shown in our experiments to evaluate the learning model and compare the two descriptors’ learning ability in the evolutionary runs. The reported results indicate that high-level descriptors are more appropriate in approximating user’s implicit aesthetic intentions for solving the problem considered.

A survey of evolutionary computation applied to finance.GAs, GP, LCS, MOEAS, EDAS and co-evolution approaches are covered.This new version makes a revision of similar surveys to refine the scope.An analysis of past and new references allowed determines changes of interest in the field.The unexplored combinations of problems and solution methods are indicated and discussed. This article presents a review of the application of evolutionary computation methods to solving financial problems. Genetic algorithms, genetic programming, multi-objective evolutionary algorithms, learning classifier systems, co-evolutionary approaches, and estimation of distribution algorithms are the techniques considered. The novelty of our approach comes in three different manners: it covers time lapses not included in other review articles, it covers problems not considered by others, and the scope covered by past and new references is compared and analyzed. The results concluded the interest about methods and problems has changed through time. Although, genetic algorithms have remained the most popular approach in the literature. There are combinations of problems and solutions methods which are yet to be investigated.

Graphical abstractDisplay Omitted HighlightsProvide an updated and systematic review of distributed evolutionary algorithms.Classify the models into population and dimension-distributed groups semantically.Analyze the parallelism, search behaviors, communication costs, scalability, etc.Highlight recent research hotspots in this field.Discuss challenges and potential research directions in this field. The increasing complexity of real-world optimization problems raises new challenges to evolutionary computation. Responding to these challenges, distributed evolutionary computation has received considerable attention over the past decade. This article provides a comprehensive survey of the state-of-the-art distributed evolutionary algorithms and models, which have been classified into two groups according to their task division mechanism. Population-distributed models are presented with master-slave, island, cellular, hierarchical, and pool architectures, which parallelize an evolution task at population, individual, or operation levels. Dimension-distributed models include coevolution and multi-agent models, which focus on dimension reduction. Insights into the models, such as synchronization, homogeneity, communication, topology, speedup, advantages and disadvantages are also presented and discussed. The study of these models helps guide future development of different and/or improved algorithms. Also highlighted are recent hotspots in this area, including the cloud and MapReduce-based implementations, GPU and CUDA-based implementations, distributed evolutionary multiobjective optimization, and real-world applications. Further, a number of future research directions have been discussed, with a conclusion that the development of distributed evolutionary computation will continue to flourish.

Evolutionary Algorithm provides a framework that is largely applicable to particular problems including multiobjective optimization problems, basically for the ease of their implementation and their capability to perform efficient parallel search. Indeed, in some cases, expensive multiobjective optimization evaluations might be a challenge to restrict the number of explicit fitness evaluations in multiobjective evolutionary algorithms. Accordingly, this article presents a novel approach that tackles this problem so as to not only decrease the number of fitness evaluations but also to improve the performance. During evolution, our proposed approach selects fit individuals based on the knowledge acquired throughout the search, and performs explicit fitness evaluations on these individuals. A comprehensive comparative analysis of a wide range of well-established test problems, selected from both traditional and state-of-the-art benchmarks, has been presented. Afterward, the effectiveness of the obtained results is compared with some of the state-of-the-art methods using two well-known metricsi.e. Hypervolume and Inverted Generational Distance (IGD). The experiments of our implemented approach is performed to illustrate that our proposal seems to be promising and would prove more efficient than other approaches in terms of both the performance and the computational cost.

© 2015 IEEE. 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.

The number of objectives in many-objective optimization problems (MaOPs) is typically high and evolutionary algorithms face severe difficulties in solving such problems. In this paper, we propose a new scalable evolutionary algorithm, called evolutionary path control strategy (EPCS), for solving MaOPs. The central component of our algorithm is the use of a reference vector that helps simultaneously minimizing all the objectives of an MaOP. In doing so, EPCS employs a new fitness assignment strategy for survival selection. This strategy consists of two procedures and our algorithm applies them sequentially. It encourages a population of solutions to follow a certain path reaching toward the Pareto optimal front. The essence of our strategy is that it reduces the number of nondominated solutions to increase selection pressure in evolution. Furthermore, unlike previous work, EPCS is able to apply the classical Pareto-dominance relation with the new fitness assignment strategy. Our algorithm has been tested extensively on several scalable test problems, namely five DTLZ problems with 5 to 40 objectives and six WFG problems with 2 to 13 objectives. Furthermore, the algorithm has been tested on six CEC09 problems having 2 or 3 objectives. The experimental results show that EPCS is capable of finding better solutions compared to other existing algorithms for problems with an increasing number of objectives.

In response to many multi-attribute decision-making (MADM) problems involved in chemical processes such as controller tuning, which suffer human's subjective preferential nature in human–computer interactions, a novel affective computing and preferential evolutionary solution is proposed to adapt human–computer interaction mechanism. Based on the stimulating response mechanism, an improved affective computing model is introduced to quantify decision maker's preference in selections of interactive evolutionary computing. In addition, the mathematical relationship between affective space and decision maker's preferences is constructed. Subsequently, a human–computer interactive preferential evolutionary algorithm for MADM problems is proposed, which deals with attribute weights and optimal solutions based on preferential evolution metrics. To exemplify applications of the proposed methods, some test functions and, emphatically, controller tuning issues associated with a chemical process are investigated, giving satisfactory results.

The theory of population genetics and evolutionary computation have been evolving separately for nearly 30 years. Many results have been independently obtained in both fields and many others are unique to its respective field. We aim to bridge this gap by developing a unifying framework for evolutionary processes that allows both evolutionary algorithms and population genetics models to be cast in the same formal framework. The framework we present here decomposes the evolutionary process into its several components in order to facilitate the identification of similarities between different models. In particular, we propose a classification of evolutionary operators based on the defining properties of the different components. We cast several commonly used operators from both fields into this common framework. Using this, we map different evolutionary and genetic algorithms to different evolutionary regimes and identify candidates with the most potential for the translation of results between the fields. This provides a unified description of evolutionary processes and represents a stepping stone towards new tools and results to both fields., Highlights • A unifying framework for evolutionary processes. • Formalizing the defining properties of the different kinds of processes:○Variation operators (mutation and recombination).○Selection operators. • Formalizing several common examples of these operators in terms of our framework. • Proving that these common operators respect the properties that we define for their class. • Casting several classical models and algorithms from both fields into our framework.

The concept of the natural computation for optimal scheduling in high level synthesis, for resource constraint and time constraint scheduling problem in automated integrated circuit synthesis using Integer Linear Programming (ILP) modeling is presented in this paper. This paper compares three natural computations paradigms: (i) evolution optimizer technique genetic algorithm, (ii) evolutionary programming, and (iii) swarm intelligence based particle swarm optimization. Experimental results indicate that evolution based Genetic Algorithm search is more powerful search compared to Evolutionary Programming and Particle Swam Optimization.

In this study, we propose an Interactive Evolutionary Computation system to design gene networks for synthetic biology, of which the objective is to construct new organisms through the artificial synthesis of gene networks. Wet laboratories typically design gene networks through trial and error methods, which makes the construction of complex networks difficult. As a solution, automation can significantly improve the efficiency of identification of useful networks. However, guidance and feedback from biologists are required for the automation of this process, primarily because models that are sufficiently accurate at precisely simulating network behavior do not exist yet. Therefore, we have implemented an Interactive Evolutionary Computation system that allows the automation of the gene network design task while incorporating expert knowledge. Our results show that the system can efficiently design oscillating networks and can successfully identify complex networks, which are difficult to generate manually.

This work investigates the effects of the periodization of local and global multi-objective search algorithms. We rely on a model for periodization and define a multi-objective evolutionary algorithm adopting concepts from Evolutionary Strategies and NSGAII. We show that our method excels for the evolution of digital circuits on the Cartesian Genetic Programming model as well as on some standard benchmarks such as the ZDT6, especially when periodized with standard multi-objective genetic algorithms.

In this paper, an open source C++ Genetic Algorithm library is proposed called openGA. This library is capable of optimization in each of single objective, multi-objective and interactive modes. The main motivation for proposing this library is to provide freedom to users for designing their custom solution data model without limitations which many currently available software/libraries suffer from such as forcing a user to define the solutions as vectors or limiting the output of evaluation functions to a predefined format. In addition, the user has the entire control over genetic operations such as solution creation, mutation and crossover. The multi-object mode performs a Non-dominated Sorting Genetic Algorithm known as NSGA-III to obtain the pareto-optimal front while preserving the solution diversity. This library can handle multi-threading computations for single and multi-objective problems to increase the speed of the calculations significantly. The interactive mode is suitable for applications where human subjectivity is involved for evaluation of the cost function. Several simulation and tests are performed to verify the effectiveness of this library for calculations of optimization problems.

In the area of product design, adding user's kansei (feelings) on the products is important for emphasizing its value. Interactive Evolutionary Computation (IEC) is well known method that searches optimal or better media contents suited for each user's kansei. Some previous studies applied IEC into problem of multiple users. Purpose of the present study is to compare Distributed Interactive Genetic Algorithm (DIGA) and Interactive Genetic Algorithm (IGA) in a view point of creating music melody. We conducted listening experiments for investigating efficiency of the DIGA and IGA for creation of music melody. Ten persons were divided into five pairs, the pair participated in the experiment as subjects simultaneously. As an index of searching space of the pair, distance of melodies between the subjects in the pair in each generation was investigated. As a result, a trend of decrease in the distance was observed in the DIGA where exchange of the best individuals between the subjects.

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In this paper we report ongoing work evolving bio-hybrid societies. We develop robots that are integrated into an animal society and accepted as conspecifics. We are using evolutionary algorithms to optimise robot controllers to affect the behaviour of animals. Fitness evaluation is the result of measuring the effect a robot controller has on these animals. Animal habituation and heterogeneous response are two factors that have a major role in this fitness evaluation. We discuss our choices in designing a fitness evaluation procedure and how using animals as fitness function providers impacts this.

The novel R package ecr (version 2), short for Evolutionary Computation in R, provides a comprehensive collection of building blocks for constructing powerful evolutionary algorithms for single- and multi-objective continuous and combinatorial optimization problems. It allows to solve standard optimization tasks with few lines of code using a black-box approach. Moreover, rapid prototyping of non-standard ideas is possible via an explicit, white-box approach. This paper describes the design principles of the package and gives some introductory examples on how to use the package in practise.

In order to reduce the number of fitness evaluations, the novel surrogate model called Rank Space Estimation (RSE) model and the surrogate-assisted EC with RSE model called the Fitness Landscape Learning Evolutionary Computation (FLLEC) have been proposed. In this paper, we analyze the scarling effect for CMA-ES with RSE model with support vector machine(SVM). The performance of CMA-ES with RSE model by using adequate scarling is shown by computer simulation taking k-table problem as an example.

We present a parallel evolutionary algorithm with interleaving generations. The algorithm uses a careful analysis of genetic operators and selection in order to evaluate individuals from following generations while the current generation is still not completely evaluated. This brings significant advantages in cases where each fitness evaluation takes different amount of time, the evaluations are performed in parallel, and a traditional generational evolutionary algorithm has to wait for all evaluations to finish. The proposed algorithm provides better utilization of computational resources in these cases. Moreover, the algorithm is functionally equivalent to the generational evolutionary algorithm, and thus it does not have any evaluation time bias, which is often present in asynchronous evolutionary algorithms.The proposed algorithm is tested in a series of simple experiments and its effectiveness is compared to the effectiveness of the generational evolutionary algorithm in terms of CPU utilization.

In the Evolutionary Computation field, it is frequent to assume that a computation load necessary for fitness value computation is, at least, similar for all possible cases. The main objective of this paper is to show that the above assumption is frequently false. Therefore, the examples of evolutionary methods that use problem encoding which allows for significant optimization of the fitness computation process are pointed out and analyzed. The definition of Problem Encoding Allowing Cheap Fitness Computation of Mutated Individuals (PEACh) is proposed. Another objective of the paper is to start a discussion concerning the computation load measurement in the evolutionary computation field. As shown, the Fitness Function Evaluation number is not always a fair measure and may be significantly affected by the quality of method implementation.

This paper proposes interactive decoration design support system introducing interactive evolutionary computation. Evaluation problem existing human can be evaluated synthesizing multi objectives. The proposed system made it importance dividing the human evaluation into two part, which is including regular level of design quality and user's subjective evaluation by affective or Kansei image, and assumed to acquire good results by executing in serial order in a certain evaluation phase. From the experimental results, effectiveness of proposed methodology involving evolution by using fitness evaluation of decoration designs for quality and evolution by user evaluation are confirmed.

Interactive evolutionary computation (IEC) is known as a method to optimize media contents suited to user's subjective feeling and preference. Previous IECs employed various evolutionary algorithms, and some of them applied Tabu Search (TS) algorithm: This method was named Interactive Tabu Search (ITS). In the ITS, users have to select the best individual from current population. ITS was often used for the area of computer graphics, and some previous studies applied ITS for creating fragrance. In these studies, blended fragrances composed of several aroma sources are corresponded to individuals in ITS. Adjusting intensity of each aroma source was target of optimization. Purpose of this study is to propose ITS that combines paired comparison in user's evaluation task and differential vector between the best individuals of different generations. The best individuals here mean that the best individuals in the current generation and in the previous generation. By combining these factors, we expect both of easy user's selection of the best individual and efficient search in searching fragrance. To investigate a fundamental efficiency of the proposed ITS, a smelling experiment was conducted. Target fragrance was a fragrance suited to a deodorant which has originally no fragrance.

In the area of product design, it is important that adding user's feelings on the products for emphasizing its value. Interactive Evolutionary Computation (IEC) is known as a method that searches optimal or better media contents suited for each user's feelings. In terms of expanding the ability of IEC, some recent studies applied IEC into problem of multiple users. This study aims to create music melody suited for multiple users' feelings by employing parallel Distributed Interactive Genetic Algorithm (DIGA). In this method, each of the users proceeds general Interactive Genetic Algorithm (IGA) process by evaluating solution candidates subjectively. In some generations, solution candidates are exchanged between the users. With the exchange, each of the users is affected by other users' feelings. As a result of these processes, obtaining good solution suited for all users. We conducted listening experiments for investigating efficiency of the DIGA for creation of music melody. Ten persons participated in the experiment as subjects, and pair of the subjects participated in the IGA task simultaneously. Experimental results show that higher fitness was obtained in the final generation, and similar melody was obtained through exchange of solutions. To clarify the efficiency of the exchange of the asynchronous method, further study with comparing experiment including conditions synchronous is needed.

Self-adaptation is an efficient way to control the strategy parameters of an Evolutionary Algorithm automatically during optimization. It is based on implicit evolutionary search in the space of strategy parameters, and has been proven to work well as on-line parameter control method for a variety of strategy parameters, from local to global ones. Our proposed Self-Adaptive Multi-Objective Evolutionary Algorithm is a two level algorithm. The proposed solution is applied on the problem of de novo molecular design. The outer level is the algorithm that is responsible for the self adaptive techniques and is based on Multi-Objective Genetic Algorithm. The inner level is based on the elite Multi-Objective Evolutionary Graph Algorithm. Both the outer and inner algorithms are variations of our previously proposed Multi-Objective Evolutionary Graph Algorithm framework. The outer Multi-Objective Genetic Algorithm operates on a chromosome of elements, while the inner elite Multi-Objective Evolutionary Graph Algorithm operates on molecular graph chromosomes. In general, the proposed solution: (i) searches a larger space, (ii) generates far more solutions per iteration, (iii) evaluates different sets of parameter options for the given problem, and (iv) proposes the fittest parameter sets that should be used for the given problem.

Various evolutionary algorithms are being developed to search the optimal solution of various problems in the real world. Evolutionary algorithms search solutions showing the optimal fitness to given problem using their own operators. Engineering benchmark problems can be used for performance measurement of evolutionary algorithms, and the water distribution network design problem is one of the widely used benchmark problems. In this study, the water distribution network design problems are generated by modifications of five problem characteristic factors. Generated benchmark problems are applied to quantitatively evaluate the performance among evolutionary algorithms. Each algorithm shows its own strength and weakness. Optimization results show that the engineering benchmark generation method suggested in this study can be served as a reliable framework for comparison of performances on various water distribution network design problems.

In evolutionary swarms adaptability and diversity are closely related concepts. In order to get a better understanding of their codependency we study a heterogeneous evolutionary multi-agent system with different rates of redundancy within the genetic material. The agents process a dynamic multi-objective task, where their genetic material defines their efficiency concerning the different objective functions of that task. One focus of this study is the influence of an elitist behavior performed by the agents during the evolutionary process, where an agent can decline the genetic material of another agent if it does not meet specific requirements. Further we analyze the impact of three different methods to aggregate the objective values into a single fitness value that is applicable for the evolutionary mechanism of the system. The results show that heterogeneity in the optimization behavior of the agents is very beneficial as it maintains a higher diversity in the system. The elitist behavior of the agents slows the evolutionary process but gives it a stronger pull towards qualitatively higher positions in the objective space. Indeed, the pace of the evolutionary process ultimately has a higher impact on the adaptability of the system than the amount of redundancy in the genetic information.

Hyper-heuristics is an emergent technology that has proven to be effective at solving real-world problems. The two main categories of hyper-heuristics are selection and generation. Selection hyper-heuristics select existing low-level heuristics while generation hyper-heuristics create new heuristics. At the inception of the field single point searches were essentially employed by selection hyper-heuristics, however as the field progressed evolutionary algorithms are becoming more prominent. Evolutionary algorithms, namely, genetic programming, have chiefly been used for generation hyper-heuristics. Implementing evolutionary algorithm hyper-heuristics can be quite a time-consuming task which is daunting for first time researchers and practitioners who want to rather focus on the application domain the hyper-heuristic will be applied to which can be quite complex. This paper presents a Java toolkit for the implementation of evolutionary algorithm hyper-heuristics, namely, EvoHyp. EvoHyp includes libraries for a genetic algorithm selection hyper-heuristic (GenAlg), a genetic programming generation hyper-heuristic (GenProg), a distributed version of GenAlg (DistrGenAlg) and a distributed version of GenProg (DistrGenProg). The paper describes the libraries and illustrates how they can be used. The ultimate aim is to provide a toolkit which a non-expert in evolutionary algorithm hyper-heuristics can use. The paper concludes with an overview of future extensions of the toolkit.

In multiobjective evolutionary algorithms, how to select the optimal solutions from the offspring candidate set significantly affects the optimization process. Usually, the selection process is largely based on the real objective values or surrogate model estimating objective values. However, these selection processes are very time consuming sometimes, especially for some real optimization problems. Recently, some researches began to employ supervised classification to assist offspring selection, but these works are difficult to prepare the exact positive and negative samples or time consuming of parameter tuning problems. In order to solve these disadvantages, we propose a decomposition based multiobjective evolutionary algorithm with semi-supervised classification. This approach using random sampling and non-dominated sorting to construct semi supervised classifier. In each generation, a set of candidate solutions are generated for each subproblem and only good solutions are reserved by classifier. If there is more than one good solutions, we calculate each of good solutions by real objective function and choose the best one as the offspring solution. Based on the typical decomposition based multiobjective evolutionary algorithm MOEA/D, we design algorithm framework through integrating the novel offspring selection process based on semi-supervised classification. Experiments show that the proposed algorithm performs best in most test cases and improves the performance of MOEA/D.