Evolutionary Computation Framework Research Articles

Optimizing Lateral Airway Offset for Collision Risk Mitigation Using Differential Evolution

A majority of aircraft are now using Global Navigation Satellite System (GNSS) for navigation. This has led to an effect of reducing the magnitude of lateral deviations from the route center line and, consequently, increasing the probability of a collision, should a loss of vertical separation between aircraft on the same route occur. The International Civil Aviation Organization (ICAO) has introduced Strategic Lateral Offset Procedures (SLOP) that allow suitably equipped aircrafts to fly with 1nmi or 2nmi lateral offset to the right of airway centerline in oceanic airspace. Very few aircraft, however, are using the SLOP procedure because of the lack of understanding of its safety benefits and implementation issues in identifying correct lateral offset that can reduce the collision risk. This paper proposes an Evolutionary Computation framework using Differential Evolution process to identify optimal lateral offsets for each airway in a given airspace such that it reduces the overall collision risk. Airway specific lateral offsets are then correlated with airway-traffic features using Multiple Regression models to identify which features can explain the optimal lateral offset. The proposed approach establishes a generic mapping that can suggest optimal lateral offsets for a given airspace based on airway-traffic features to mitigate collision risk. The proposed methodology is applied to Collision Risk assessment of one-day traffic data (710 flights) in Bahrain Upper Airspace (FL290-FL410) to estimate optimal lateral offset that resulted in significant reduction of collision risk. Further, the number of flights and crossings on an airway were identified as key features affecting optimal lateral offset.

CPG Network Optimization for a Biomimetic Robotic Fish via PSO.

In this brief, we investigate the parameter optimization issue of a central pattern generator (CPG) network governed forward and backward swimming for a fully untethered, multijoint biomimetic robotic fish. Considering that the CPG parameters are tightly linked to the propulsive performance of the robotic fish, we propose a method for determination of relatively optimized control parameters. Within the framework of evolutionary computation, we use a combination of dynamic model and particle swarm optimization (PSO) algorithm to seek the CPG characteristic parameters for an enhanced performance. The PSO-based optimization scheme is validated with extensive experiments conducted on the actual robotic fish. Noticeably, the optimized results are shown to be superior to previously reported forward and backward swimming speeds.

Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks.

Microarray technologies have been the basis of numerous important findings regarding gene expression in the few last decades. Studies have generated large amounts of data describing various processes, which, due to the existence of public databases, are widely available for further analysis. Given their lower cost and higher maturity compared to newer sequencing technologies, these data continue to be produced, even though data quality has been the subject of some debate. However, given the large volume of data generated, integration can help overcome some issues related, e.g., to noise or reduced time resolution, while providing additional insight on features not directly addressed by sequencing methods. Here, we present an integration test case based on public Drosophila melanogaster datasets (gene expression, binding site affinities, known interactions). Using an evolutionary computation framework, we show how integration can enhance the ability to recover transcriptional gene regulatory networks from these data, as well as indicating which data types are more important for quantitative and qualitative network inference. Our results show a clear improvement in performance when multiple datasets are integrated, indicating that microarray data will remain a valuable and viable resource for some time to come.

機械学習アルゴリズムを導入した適応度景観推定型進化型計算の提案

One of the most important issues for evolutionary computation (EC) is to consider fitness landscape and the number of fitness evaluations. Especially, reducing the number of fitness evaluations is required in applications of EC to various kind of problems. In this paper, we proposed a novel EC framework called the Fitness Landscape Learning Evolutionary Computation: FLLEC with surrogate model which can predict the ranks of two individuals using SVM. The effectiveness of the proposed method is confirmed by computer simulation taking an Nk-landscape problem and a knapsack problem as examples by Air GA which is one of the FLLEC.

Short Note on Computational Intelligent Techniques for Industrial Production Systems

Computational intelligent has been accepted and recognized by global research scientists, decision makers and practicing researchers in recent years as powerful artificial intelligent techniques, for solving unlimited number of complex real-world problems particularly related to research area of unpredictable and uncertain optimization. Under the uncertain and turbulence environment, classical and traditional approaches are unable to obtain a complete satisfactory solution with higher level of degree of satisfaction for the real world practical problems on optimization. Therefore, new global computational intelligent optimization methods are required to handle these issues seriously. One such method is hybrid evolutionary computation, a generic, flexible, robust, beneficial and versatile framework for solving complex and unpredictable problems of global optimization and search in real world application problems.

Articulated Human Motion Tracking Using Sequential Immune Genetic Algorithm

We formulate human motion tracking as a high-dimensional constrained optimization problem. A novel generative method is proposed for human motion tracking in the framework of evolutionary computation. The main contribution is that we introduce immune genetic algorithm (IGA) for pose optimization in latent space of human motion. Firstly, we perform human motion analysis in the learnt latent space of human motion. As the latent space is low dimensional and contents the prior knowledge of human motion, it makes pose analysis more efficient and accurate. Then, in the search strategy, we apply IGA for pose optimization. Compared with genetic algorithm and other evolutionary methods, its main advantage is the ability to use the prior knowledge of human motion. We design an IGA-based method to estimate human pose from static images for initialization of motion tracking. And we propose a sequential IGA (S-IGA) algorithm for motion tracking by incorporating the temporal continuity information into the traditional IGA. Experimental results on different videos of different motion types show that our IGA-based pose estimation method can be used for initialization of motion tracking. The S-IGA-based motion tracking method can achieve accurate and stable tracking of 3D human motion.

Generative tracking of 3D human motion in latent space by sequential clonal selection algorithm

High dimensional pose state space is the main challenge in articulated human pose tracking which makes pose analysis computationally expensive or even infeasible. In this paper, we propose a novel generative approach in the framework of evolutionary computation, by which we try to widen the bottleneck with effective search strategy embedded in the extracted state subspace. Firstly, we use ISOMAP to learn the low-dimensional latent space of pose state in the aim of both reducing dimensionality and extracting the prior knowledge of human motion simultaneously. Then, we propose a manifold reconstruction method to establish smooth mappings between the latent space and original space, which enables us to perform pose analysis in the latent space. In the search strategy, we adopt a new evolutionary approach, clonal selection algorithm (CSA), for pose optimization. We design a CSA based method to estimate human pose from static image, which can be used for initialization of motion tracking. In order to make CSA suitable for motion tracking, we propose a sequential CSA (S-CSA) algorithm by incorporating the temporal continuity information into the traditional CSA. Actually, in a Bayesian inference view, the sequential CSA algorithm is in essence a multilayer importance sampling based particle filter. Our methods are demonstrated in different motion types and different image sequences. Experimental results show that our CSA based pose estimation method can achieve viewpoint invariant 3D pose reconstruction and the S-CSA based motion tracking method can achieve accurate and stable tracking of 3D human motion.

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.

Selected Papers from NaBIC 2010

The Second World Congress on Nature and Biologically Inspired Computing (NaBIC2010) was held at the Kitakyushu International Conference Center December 15-17, 2010, in Kitakyushu, Japan. NaBIC2010 provided a forum for researchers, engineers, and students from worldwide to discuss state-of-the-art machine intelligence and to address issues related to building human-friendly machines by learning from nature. NaBIC2010 covered a wide range of studies ? from theoretical and algorithmic studies on nature and biologically inspired computing techniques to their real-world applications. Top researchers presenting papers at NaBIC2010 were invited to contribute to this special issue. Through a fair peer review process, four extended papers have been accepted ? an acceptance rate of 50%. The first paper entitled gA Study on Computational Efficiency and Plasticity in Baldwinian Learningh by Liu and Iba analyzes Baldwinian evolution efficiency by comparing it to alternatives such as standard Darwinian evolution with no learning, Lamarckian evolution, and Baldwinian evolution with different learning and plasticity evolution. The second paper entitled gExperimental Study of a Structured Differential Evolution with Mixed Strategiesh by Ishimizu and Tagawa proposes island-based DE with ring or torus networks. The authors examine the performance of the proposed DE with the effects of different strategies. The third paper entitled gMulti-Space Competitive DGA for Model Selection and its Application to Localization of Multiple Signal Sourcesh by Ishikawa, Misawa, Kubota, Tokiwa, Horio, and Yamakawa proposes a distributed genetic algorithm in which each subpopulation searches for a solution in different decision space. Subpopulations change size based on search progress. The fourth paper entitled gAn Extended Interactive Evolutionary Computation Using Heart Rate Variability as Fitness Value for Composing Music Chord Progressh by Fukumoto, Nakashima, Ogawa, and Imai uses heart-rate variability instead of direct human evaluations in an interactive evolutionary computation framework. As guest editors of this special issue, we would like to thank the authors for their unique and interesting contributions and the reviewers for their careful checking and invaluable comments.

Interactive Multiple Criteria Decision Making based on preference driven Evolutionary Multiobjective Optimization with controllable accuracy

We present an approach to interactive Multiple Criteria Decision Making based on preference driven Evolutionary Multiobjective Optimization with controllable accuracy. The approach relies on formulae for lower and upper bounds on coordinates of the outcome of an arbitrary efficient variant corresponding to preference information expressed by the Decision Maker. In contrast to earlier works on that subject, here lower and upper bounds can be calculated and their accuracy controlled entirely within evolutionary computation framework. This is made possible by exploration of not only the region of feasible variants – a standard within evolutionary optimization, but also the region of infeasible variants, the latter to our best knowledge being a novel approach within Evolutionary Multiobjective Optimization. To illustrate how this concept can be applied to interactive Multiple Criteria Decision Making, two algorithms employing evolutionary computations are proposed and their usefulness demonstrated by a numerical example.

Optimization Algorithm for Solving Degree-Constrained Minimum Spanning Tree Problem

为求解大规模结点度约束最小生成树问题,提出一种带有嫁接和剪接算子操作的优化算法.通过借鉴花草果树种植技术,建立一种以基本遗传算子为基础、带有加速和调节算子作为激励的进化计算体系;嫁接以一种贪婪的思想加速搜索,按收益最大化原则进行剪接.对可能陷入局部极值引起冲突的现象及冲突检测的方法进行分析,并提出了冲突的若干解决方法.针对DCMST问题求解中的复杂性,提出了几种有效的嫁接和剪接的策略,并对算法的收敛性和计算复杂度进行了分析.通过该算法对结点数为50~500之间的Euclidean问题和按均匀随机方式产生的non-Euclidean度约束最小生成树问题进行求解.与现有文献的实验结果对比表明,该方法在求解最好解的精度和收敛速度上均有一定的优势.;To solve the degree-constrained spanning minimum tree (DCMST) problems with a large scale of nodes, an optimization algorithm based on grafting and pruning operator is proposed. Learning from the flower planting techniques, this paper establishes, an evolutionary computation framework containing accelerating and adjusting operators based on conventional genetic operators. The grafting and pruning are performed by a greedy strategy and gain maximization respectively. The collision caused by possible local minima is analyzed and detected, and several methods dealing with the collision are discussed. To tackle the complexity of DCMST problems, some strategies of grafting and pruning are proposed. The convergence of the proposed algorithm and the computation complexity are analyzed. For DCMST problems of Euclidean and uniform random non-Euclidean instances from 50 to 500 nodes, the experiments show that the quality and convergence rate of the proposed method are the best compared with the known results.

Generation of Neural Network Models of the Brain by Evolutionary Computation

Event Abstract Back to Event Generation of Neural Network Models of the Brain by Evolutionary Computation Masayuki Kikuchi1* and Kousei Watanabe2 1 Tokyo University of Technology, School of Computer Science, Japan 2 Tokyo University of Technology, Graduate School of Bionics, Computer and Media Sciences, Japan Brain science has been ceaselessly progressing by propulsive forces of several research methodologies. Obviously one of the most important approaches is modeling. Especially neural network models have been giving explanations of information processing in the neural system, which can consistently unify a large number of experimental findings on the regions of electrophysiology, fMRI, psychophysics, and so on. One of the important factors of the conventional modeling approach is that the models had constructed by human modeling researchers, and the principles of the models had been inspired due to the instincts and experiences of the modelers. Especially there has been implicit agreement that model should be represented concisely. On the other hand, the scale of the actual brain is very huge, and the network structures and connection pattern between neurons are quite complex. Accordingly, it might be happen that the models constructed by human researchers fail to account the actual principles of information processing in the brain. The modeling approach should overcome the restriction of modeler’s inspiration in order to successfully catch the real nature of the brain. This study adopts a new method to construct the neural network models utilizing the framework of evolutionary computation. The neural networks are generated automatically by computer, instead of human modelers (i.e. the authors). We use genetic algorithm (GA) in order to determine the network structure, with some constrains as for the global architecture revealed by physiology and anatomy. In our framework, each gene on the GA computation corresponds to a neural network, and a lot of neural networks coexist in a search space at the same time. Fitness function is defined as the behavioral similarity of each neural network and actual characteristics of the brain. We adopt this method to generate a neural network model of the visual system. We currently focus on the visual functions of figure/ground separation, and pattern recognition. There are some preceding studies about the hybrid method of neural network and GA. However, the purposes of almost all of them were to offer multipurpose adaptive method applicable for any engineering scene. In contrast, we focus on the method of automatic generation of models for the purpose of reducing arbitrariness of the human researchers’ inspiration. Keywords: computational neuroscience, General neuroinformatics Conference: 4th INCF Congress of Neuroinformatics, Boston, United States, 4 Sep - 6 Sep, 2011. Presentation Type: Poster Presentation Topic: General neuroinformatics Citation: Kikuchi M and Watanabe K (2011). Generation of Neural Network Models of the Brain by Evolutionary Computation. Front. Neuroinform. Conference Abstract: 4th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2011.08.00127 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 17 Oct 2011; Published Online: 19 Oct 2011. * Correspondence: Dr. Masayuki Kikuchi, Tokyo University of Technology, School of Computer Science, Tokyo, Japan, kikuchi@cs.teu.ac.jp Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Masayuki Kikuchi Kousei Watanabe Google Masayuki Kikuchi Kousei Watanabe Google Scholar Masayuki Kikuchi Kousei Watanabe PubMed Masayuki Kikuchi Kousei Watanabe Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Toward an Evolutionary Computing Modeling Language

The importance of domain knowledge in the design of effective evolutionary algorithms (EAs) is widely acknowledged in the meta-heuristics community. In the last few decades, a plethora of EAs has been manually designed by domain experts for solving domain-specific problems. Specialization has been achieved mainly by embedding available domain knowledge into the algorithms. Although programming libraries have been made available to construct EAs, a unifying framework for designing specialized EAs across different problem domains and branches of evolutionary computing does not exist yet. In this paper, we address this issue by introducing an evolutionary computing modeling language (ECML) which is based on the unified modeling language (UML). ECML incorporates basic UML elements and introduces new extensions that are specially needed for the evolutionary computation domain. Subsequently, the concept of meta evolutionary algorithms (MEAs) is introduced as a family of EAs that is capable of interpreting ECML. MEAs are solvers that are not restricted to a particular problem domain or branch of evolutionary computing through the use of ECML. By separating problem-specific domain knowledge from the EA implementation, we show that a unified framework for evolutionary computation can be attained. We demonstrate our approach by applying it to a number of examples.

Algorithm quasi‐optimal (AQ) learning

AbstractThe algorithm quasi‐optimal (AQ) is a powerful machine learning methodology aimed at learning symbolic decision rules from a set of examples and counterexamples. It was first proposed in the late 1960s to solve the Boolean function satisfiability problem and further refined over the following decade to solve the general covering problem. In its newest implementations, it is a powerful but yet little explored methodology for symbolic machine learning classification. It has been applied to solve several problems from different domains, including the generation of individuals within an evolutionary computation framework. The current article introduces the main concepts of the AQ methodology and describes AQ for source detection(AQ4SD), a tailored implementation of the AQ methodology to solve the problem of finding the sources of atmospheric releases using distributed sensor measurements. The AQ4SD program is tested to find the sources of all the releases of the prairie grass field experiment. Copyright © 2010 John Wiley & Sons, Inc.This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Knowledge Discovery Statistical Learning and Exploratory Methods of the Data Sciences > Rule-Based Mining

Evolutionary computation system for musical composition using listener's heartbeat information

AbstractInteractive evolutionary computation (EC) creates media contents that fit on a user; however, user's fatigue problem still remains. Takagi et al. proposed a framework of EC that evaluates media contents based on a user's physiological indices, and this technique is expected to solve the problem. In this study, we constructed an EC system that creates musical melody using listener's heartbeat intervals. Based on the constructed system, we proposed a method that creates media contents and presents them to a user automatically using blank. The constructed system and the proposed method have the possibility to reduce user's fatigue. Copyright © 2008 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

A fast and accurate approach for 3D image registration using the scatter search evolutionary algorithm

Nowadays, image registration (IR) is still an important and useful task in several areas such as remote sensing, medicine, cartography, and computer vision. Different approaches to solve the existing variants of the problem are commonly proposed in the specialized literature. In this paper, we focus our interest on the 3D IR problem considering similarity transformations and our proposal is based on the use of a new procedure based on the evolutionary computation framework for non-linear optimization. We apply an emergent global optimization strategy called scatter search providing a fast and accurate algorithm. To measure its performance, we design an experimental setup considering some of the most accepted and accurate classical and evolutionary techniques for the problem, as well as six different shapes, one synthetic and five magnetic resonance images, dealing with different levels of noise and occlusion in the scenarios treated.

Self-Organized Modularization in Evolutionary Algorithms

The principle of modularization has proven to be extremely successful in the field of technical applications and particularly for Software Engineering purposes. The question to be answered within the present article is whether mechanisms can also be identified within the framework of Evolutionary Computation that cause a modularization of solutions. We will concentrate on processes, where modularization results only from the typical evolutionary operators, i.e. selection and variation by recombination and mutation (and not, e.g., from special modularization operators). This is what we call Self-Organized Modularization. Based on a combination of two formalizations by Radcliffe and Altenberg, some quantitative measures of modularity are introduced. Particularly, we distinguish Built-in Modularity as an inherent property of a genotype and Effective Modularity, which depends on the rest of the population. These measures can easily be applied to a wide range of present Evolutionary Computation models. It will be shown, both theoretically and by simulation, that under certain conditions, Effective Modularity (as defined within this paper) can be a selection factor. This causes Self-Organized Modularization to take place. The experimental observations emphasize the importance of Effective Modularity in comparison with Built-in Modularity. Although the experimental results have been obtained using a minimalist toy model, they can lead to a number of consequences for existing models as well as for future approaches. Furthermore, the results suggest a complex self-amplification of highly modular equivalence classes in the case of respected relations. Since the well-known Holland schemata are just the equivalence classes of respected relations in most Simple Genetic Algorithms, this observation emphasizes the role of schemata as Building Blocks (in comparison with arbitrary subsets of the search space).

Evolving RBF neural networks for time-series forecasting with EvRBF

This paper is focused on determining the parameters of radial basis function neural networks (number of neurons, and their respective centers and radii) automatically. While this task is often done by hand, or based in hillclimbing methods which are highly dependent on initial values, in this work, evolutionary algorithms are used to automatically build a radial basis function neural networks (RBF NN) that solves a specified problem, in this case related to currency exchange rates forecasting. The evolutionary algorithm EvRBF has been implemented using the evolutionary computation framework evolving object, which allows direct evolution of problem solutions. Thus no internal representation is needed, and specific solution domain knowledge can be used to construct specific evolutionary operators, as well as cost or fitness functions. Results obtained are compared with existent bibliography, showing an improvement over the published methods.

An inductive approach to ecological time series modelling by evolutionary computation

Building time series models for ecological systems that can be physically interpreted is important both for understanding the dynamics of these natural systems and the development of decision support systems. This work describes the application of an evolutionary computation framework for the discovery of predictive equations and rules for phytoplankton abundance in freshwater lakes from time series data. The suggested framework evolves several different equations and rules, based on limnological and climate variables. The results demonstrate that non-linear processes in natural systems may be successfully modelled through the use of evolutionary computation techniques. Further, it shows that a grammar based genetic programming system may be used as a tool for exploring the driving processes underlying freshwater system dynamics.

Assessing the Relevance of Processing Building Blocks in Evolutionary Computation: Experiments with Linear Systems of Equations

Experiments are conducted to assess the utility of processing building blocks within a framework of evolutionary computation. Systems of linear equations are used for testing the efficiency of different recombination operators, including one- and two-point and uniform crossover. The consistent results indicate that uniform crossover, which disrupts building blocks maximally, generates statistically significantly better solutions than one- or two-point crossover. Moreover, for the cases of small population sizes, crossing over existing solutions with completely random solutions (i.e., macromutation) can perform as well or better than the traditional oneand two-point operators. The results do not support the building block hypothesis.